Note
Go to the end to download the full example code.
Fit a 2D PRF model¶
Here we fit a 2D PRF model to data from the Szinte (2024)-dataset.
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from IPython.display import HTML, display
from braincoder.utils.data import load_szinte2024
import numpy as np
import pandas as pd
# Load data
data = load_szinte2024()
stimulus = data['stimulus']
grid_coordinates = data['grid_coordinates']
# Set up a function to draw a single frame
def update(frame):
plt.clf() # Clear the current figure
plt.imshow(stimulus[frame, :, :].T, cmap='viridis')
plt.title(f"Frame {frame}")
# Create the animation
fig = plt.figure()
ani = FuncAnimation(fig, update, frames=range(stimulus.shape[0]), interval=100)
# Convert to HTML for easy display
HTML(ani.to_html5_video())
# Set up a PRF model
# Now we will set up fake PRFs just to show how the data is structured
# We make a 9-by-9 grid of simulated PRFs
x, y = np.meshgrid(np.linspace(-6, 6, 3), np.linspace(-4, 4, 3))
# Set them up in a parameter table
# All PRFs have the same baseline and amplitude
from braincoder.models import GaussianPRF2DWithHRF
from braincoder.hrf import SPMHRFModel
parameters = pd.DataFrame({'x':x.ravel(),
'y':y.ravel(),
'sd':2.5,
'baseline':0.0,
'amplitude':1.}).astype(np.float32)
model = GaussianPRF2DWithHRF(grid_coordinates=grid_coordinates,
paradigm=stimulus,
parameters=parameters,
hrf_model=SPMHRFModel(tr=data['tr']))
# Let's plot all the RFs
rfs = model.get_rf(as_frame=True)
for i, rf in rfs.groupby(level=0):
plt.subplot(3, 3, i+1)
plt.title(f'RF {i+1}')
plt.imshow(rf.unstack('y').loc[i].T)
plt.axis('off')
# We simulate data for the given paradigm and parameters and plot the resulting time series
import seaborn as sns
data = model.simulate(noise=1.)
data.columns.set_names('voxel', inplace=True)
tmp = data.stack().to_frame('activity')
sns.relplot(x=data.index.name or 'index', y='activity', data=tmp.reset_index(), hue='voxel', kind='line', palette=sns.color_palette('tab10', n_colors=parameters.shape[0]), aspect=2.)
<seaborn.axisgrid.FacetGrid object at 0x30f388d10>
# We can also fit parameters back to data
from braincoder.optimize import ParameterFitter
# We set up a parameter fitter
par_fitter = ParameterFitter(model, data, stimulus)
# We set up a grid of parameters to search over
x = np.linspace(-8, 8, 20)
y = np.linspace(-4, 4, 20)
sd = np.linspace(1, 5, 10)
# For now, we only use one amplitude and baseline, because we
# use a correlation cost function, which is indifferent to
# the overall scaling of the model
# We can easily estimate these later using OLS
amplitudes = [1.0]
baseline = [0.0]
# Note that the grids should be given in the correct order (can be found back in
# model.parameter_labels)
grid_pars = par_fitter.fit_grid(x, y, sd, baseline, amplitudes, use_correlation_cost=True)
# Once we have the best parameters from the grid, we can optimize the baseline
# and amplitude
refined_grid_pars = par_fitter.refine_baseline_and_amplitude(grid_pars)
# We get the explained variance of these parameters
from braincoder.utils import get_rsq
refined_grid_r2 = get_rsq(data, model.predict(parameters=refined_grid_pars))
# Now we use gradient descent to further optimize the parameters
pars = par_fitter.fit(init_pars=refined_grid_pars, learning_rate=1e-2, max_n_iterations=5000,
min_n_iterations=100,
r2_atol=0.0001)
fitted_r2 = get_rsq(data, model.predict(parameters=pars))
# The fitted R2s tend to be a bit better than the grid R2s
display(refined_grid_r2.to_frame('r2').join(fitted_r2.to_frame('r2'), lsuffix='_grid', rsuffix='_fitted'))
# The real parameters are very similar to the estimated parameters
display(pars.join(parameters, lsuffix='_fit', rsuffix='_true'))
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Current R2: 0.10297/Best R2: 0.10297: 0%| | 0/5000 [00:00<?, ?it/s]
Current R2: 0.10297/Best R2: 0.10297: 0%| | 3/5000 [00:00<03:32, 23.56it/s]
Current R2: 0.10305/Best R2: 0.10306: 0%| | 3/5000 [00:00<03:32, 23.56it/s]
Current R2: 0.10316/Best R2: 0.10316: 0%| | 3/5000 [00:00<03:32, 23.56it/s]
Current R2: 0.10329/Best R2: 0.10329: 0%| | 3/5000 [00:00<03:32, 23.56it/s]
Current R2: 0.10329/Best R2: 0.10329: 0%| | 6/5000 [00:00<03:18, 25.14it/s]
Current R2: 0.10340/Best R2: 0.10340: 0%| | 6/5000 [00:00<03:18, 25.14it/s]
Current R2: 0.10350/Best R2: 0.10351: 0%| | 6/5000 [00:00<03:18, 25.14it/s]
Current R2: 0.10360/Best R2: 0.10360: 0%| | 6/5000 [00:00<03:18, 25.14it/s]
Current R2: 0.10360/Best R2: 0.10360: 0%| | 9/5000 [00:00<03:11, 26.01it/s]
Current R2: 0.10369/Best R2: 0.10369: 0%| | 9/5000 [00:00<03:11, 26.01it/s]
Current R2: 0.10379/Best R2: 0.10379: 0%| | 9/5000 [00:00<03:11, 26.01it/s]
Current R2: 0.10389/Best R2: 0.10389: 0%| | 9/5000 [00:00<03:11, 26.01it/s]
Current R2: 0.10389/Best R2: 0.10389: 0%| | 12/5000 [00:00<03:09, 26.31it/s]
Current R2: 0.10399/Best R2: 0.10399: 0%| | 12/5000 [00:00<03:09, 26.31it/s]
Current R2: 0.10408/Best R2: 0.10408: 0%| | 12/5000 [00:00<03:09, 26.31it/s]
Current R2: 0.10417/Best R2: 0.10417: 0%| | 12/5000 [00:00<03:09, 26.31it/s]
Current R2: 0.10417/Best R2: 0.10417: 0%| | 15/5000 [00:00<03:09, 26.32it/s]
Current R2: 0.10426/Best R2: 0.10426: 0%| | 15/5000 [00:00<03:09, 26.32it/s]
Current R2: 0.10435/Best R2: 0.10435: 0%| | 15/5000 [00:00<03:09, 26.32it/s]
Current R2: 0.10444/Best R2: 0.10444: 0%| | 15/5000 [00:00<03:09, 26.32it/s]
Current R2: 0.10444/Best R2: 0.10444: 0%| | 18/5000 [00:00<03:10, 26.13it/s]
Current R2: 0.10453/Best R2: 0.10453: 0%| | 18/5000 [00:00<03:10, 26.13it/s]
Current R2: 0.10461/Best R2: 0.10461: 0%| | 18/5000 [00:00<03:10, 26.13it/s]
Current R2: 0.10469/Best R2: 0.10469: 0%| | 18/5000 [00:00<03:10, 26.13it/s]
Current R2: 0.10469/Best R2: 0.10469: 0%| | 21/5000 [00:00<03:12, 25.88it/s]
Current R2: 0.10478/Best R2: 0.10478: 0%| | 21/5000 [00:00<03:12, 25.88it/s]
Current R2: 0.10486/Best R2: 0.10486: 0%| | 21/5000 [00:00<03:12, 25.88it/s]
Current R2: 0.10494/Best R2: 0.10494: 0%| | 21/5000 [00:00<03:12, 25.88it/s]
Current R2: 0.10494/Best R2: 0.10494: 0%| | 24/5000 [00:00<03:11, 25.97it/s]
Current R2: 0.10502/Best R2: 0.10502: 0%| | 24/5000 [00:00<03:11, 25.97it/s]
Current R2: 0.10510/Best R2: 0.10510: 0%| | 24/5000 [00:01<03:11, 25.97it/s]
Current R2: 0.10518/Best R2: 0.10518: 0%| | 24/5000 [00:01<03:11, 25.97it/s]
Current R2: 0.10518/Best R2: 0.10518: 1%| | 27/5000 [00:01<03:11, 25.97it/s]
Current R2: 0.10526/Best R2: 0.10526: 1%| | 27/5000 [00:01<03:11, 25.97it/s]
Current R2: 0.10533/Best R2: 0.10533: 1%| | 27/5000 [00:01<03:11, 25.97it/s]
Current R2: 0.10541/Best R2: 0.10541: 1%| | 27/5000 [00:01<03:11, 25.97it/s]
Current R2: 0.10541/Best R2: 0.10541: 1%| | 30/5000 [00:01<03:12, 25.81it/s]
Current R2: 0.10548/Best R2: 0.10548: 1%| | 30/5000 [00:01<03:12, 25.81it/s]
Current R2: 0.10556/Best R2: 0.10556: 1%| | 30/5000 [00:01<03:12, 25.81it/s]
Current R2: 0.10563/Best R2: 0.10563: 1%| | 30/5000 [00:01<03:12, 25.81it/s]
Current R2: 0.10563/Best R2: 0.10563: 1%| | 33/5000 [00:01<03:14, 25.56it/s]
Current R2: 0.10570/Best R2: 0.10570: 1%| | 33/5000 [00:01<03:14, 25.56it/s]
Current R2: 0.10577/Best R2: 0.10577: 1%| | 33/5000 [00:01<03:14, 25.56it/s]
Current R2: 0.10585/Best R2: 0.10585: 1%| | 33/5000 [00:01<03:14, 25.56it/s]
Current R2: 0.10585/Best R2: 0.10585: 1%| | 36/5000 [00:01<03:30, 23.58it/s]
Current R2: 0.10592/Best R2: 0.10592: 1%| | 36/5000 [00:01<03:30, 23.58it/s]
Current R2: 0.10598/Best R2: 0.10599: 1%| | 36/5000 [00:01<03:30, 23.58it/s]
Current R2: 0.10605/Best R2: 0.10605: 1%| | 36/5000 [00:01<03:30, 23.58it/s]
Current R2: 0.10605/Best R2: 0.10605: 1%| | 39/5000 [00:01<03:27, 23.87it/s]
Current R2: 0.10612/Best R2: 0.10612: 1%| | 39/5000 [00:01<03:27, 23.87it/s]
Current R2: 0.10619/Best R2: 0.10619: 1%| | 39/5000 [00:01<03:27, 23.87it/s]
Current R2: 0.10626/Best R2: 0.10626: 1%| | 39/5000 [00:01<03:27, 23.87it/s]
Current R2: 0.10626/Best R2: 0.10626: 1%| | 42/5000 [00:01<03:22, 24.54it/s]
Current R2: 0.10632/Best R2: 0.10632: 1%| | 42/5000 [00:01<03:22, 24.54it/s]
Current R2: 0.10639/Best R2: 0.10639: 1%| | 42/5000 [00:01<03:22, 24.54it/s]
Current R2: 0.10645/Best R2: 0.10645: 1%| | 42/5000 [00:01<03:22, 24.54it/s]
Current R2: 0.10645/Best R2: 0.10645: 1%| | 45/5000 [00:01<03:32, 23.27it/s]
Current R2: 0.10651/Best R2: 0.10652: 1%| | 45/5000 [00:01<03:32, 23.27it/s]
Current R2: 0.10658/Best R2: 0.10658: 1%| | 45/5000 [00:01<03:32, 23.27it/s]
Current R2: 0.10664/Best R2: 0.10664: 1%| | 45/5000 [00:01<03:32, 23.27it/s]
Current R2: 0.10664/Best R2: 0.10664: 1%| | 48/5000 [00:01<03:24, 24.21it/s]
Current R2: 0.10670/Best R2: 0.10670: 1%| | 48/5000 [00:01<03:24, 24.21it/s]
Current R2: 0.10676/Best R2: 0.10676: 1%| | 48/5000 [00:02<03:24, 24.21it/s]
Current R2: 0.10682/Best R2: 0.10682: 1%| | 48/5000 [00:02<03:24, 24.21it/s]
Current R2: 0.10682/Best R2: 0.10682: 1%| | 51/5000 [00:02<03:23, 24.38it/s]
Current R2: 0.10688/Best R2: 0.10688: 1%| | 51/5000 [00:02<03:23, 24.38it/s]
Current R2: 0.10693/Best R2: 0.10693: 1%| | 51/5000 [00:02<03:23, 24.38it/s]
Current R2: 0.10699/Best R2: 0.10699: 1%| | 51/5000 [00:02<03:23, 24.38it/s]
Current R2: 0.10699/Best R2: 0.10699: 1%| | 54/5000 [00:02<03:20, 24.71it/s]
Current R2: 0.10705/Best R2: 0.10705: 1%| | 54/5000 [00:02<03:20, 24.71it/s]
Current R2: 0.10710/Best R2: 0.10710: 1%| | 54/5000 [00:02<03:20, 24.71it/s]
Current R2: 0.10715/Best R2: 0.10715: 1%| | 54/5000 [00:02<03:20, 24.71it/s]
Current R2: 0.10715/Best R2: 0.10715: 1%| | 57/5000 [00:02<03:19, 24.80it/s]
Current R2: 0.10721/Best R2: 0.10721: 1%| | 57/5000 [00:02<03:19, 24.80it/s]
Current R2: 0.10726/Best R2: 0.10726: 1%| | 57/5000 [00:02<03:19, 24.80it/s]
Current R2: 0.10731/Best R2: 0.10731: 1%| | 57/5000 [00:02<03:19, 24.80it/s]
Current R2: 0.10731/Best R2: 0.10731: 1%| | 60/5000 [00:02<03:16, 25.08it/s]
Current R2: 0.10736/Best R2: 0.10736: 1%| | 60/5000 [00:02<03:16, 25.08it/s]
Current R2: 0.10741/Best R2: 0.10741: 1%| | 60/5000 [00:02<03:16, 25.08it/s]
Current R2: 0.10745/Best R2: 0.10745: 1%| | 60/5000 [00:02<03:16, 25.08it/s]
Current R2: 0.10745/Best R2: 0.10745: 1%|▏ | 63/5000 [00:02<03:13, 25.51it/s]
Current R2: 0.10750/Best R2: 0.10750: 1%|▏ | 63/5000 [00:02<03:13, 25.51it/s]
Current R2: 0.10755/Best R2: 0.10755: 1%|▏ | 63/5000 [00:02<03:13, 25.51it/s]
Current R2: 0.10759/Best R2: 0.10759: 1%|▏ | 63/5000 [00:02<03:13, 25.51it/s]
Current R2: 0.10759/Best R2: 0.10759: 1%|▏ | 66/5000 [00:02<03:11, 25.71it/s]
Current R2: 0.10763/Best R2: 0.10763: 1%|▏ | 66/5000 [00:02<03:11, 25.71it/s]
Current R2: 0.10768/Best R2: 0.10768: 1%|▏ | 66/5000 [00:02<03:11, 25.71it/s]
Current R2: 0.10772/Best R2: 0.10772: 1%|▏ | 66/5000 [00:02<03:11, 25.71it/s]
Current R2: 0.10772/Best R2: 0.10772: 1%|▏ | 69/5000 [00:02<03:09, 26.03it/s]
Current R2: 0.10776/Best R2: 0.10776: 1%|▏ | 69/5000 [00:02<03:09, 26.03it/s]
Current R2: 0.10780/Best R2: 0.10780: 1%|▏ | 69/5000 [00:02<03:09, 26.03it/s]
Current R2: 0.10784/Best R2: 0.10784: 1%|▏ | 69/5000 [00:02<03:09, 26.03it/s]
Current R2: 0.10784/Best R2: 0.10784: 1%|▏ | 72/5000 [00:02<03:08, 26.12it/s]
Current R2: 0.10788/Best R2: 0.10788: 1%|▏ | 72/5000 [00:02<03:08, 26.12it/s]
Current R2: 0.10792/Best R2: 0.10792: 1%|▏ | 72/5000 [00:02<03:08, 26.12it/s]
Current R2: 0.10795/Best R2: 0.10795: 1%|▏ | 72/5000 [00:02<03:08, 26.12it/s]
Current R2: 0.10795/Best R2: 0.10795: 2%|▏ | 75/5000 [00:02<03:09, 25.94it/s]
Current R2: 0.10799/Best R2: 0.10799: 2%|▏ | 75/5000 [00:03<03:09, 25.94it/s]
Current R2: 0.10803/Best R2: 0.10803: 2%|▏ | 75/5000 [00:03<03:09, 25.94it/s]
Current R2: 0.10806/Best R2: 0.10806: 2%|▏ | 75/5000 [00:03<03:09, 25.94it/s]
Current R2: 0.10806/Best R2: 0.10806: 2%|▏ | 78/5000 [00:03<03:08, 26.08it/s]
Current R2: 0.10810/Best R2: 0.10810: 2%|▏ | 78/5000 [00:03<03:08, 26.08it/s]
Current R2: 0.10813/Best R2: 0.10813: 2%|▏ | 78/5000 [00:03<03:08, 26.08it/s]
Current R2: 0.10817/Best R2: 0.10817: 2%|▏ | 78/5000 [00:03<03:08, 26.08it/s]
Current R2: 0.10817/Best R2: 0.10817: 2%|▏ | 81/5000 [00:03<03:10, 25.89it/s]
Current R2: 0.10820/Best R2: 0.10820: 2%|▏ | 81/5000 [00:03<03:10, 25.89it/s]
Current R2: 0.10823/Best R2: 0.10823: 2%|▏ | 81/5000 [00:03<03:10, 25.89it/s]
Current R2: 0.10826/Best R2: 0.10826: 2%|▏ | 81/5000 [00:03<03:10, 25.89it/s]
Current R2: 0.10826/Best R2: 0.10826: 2%|▏ | 84/5000 [00:03<03:08, 26.02it/s]
Current R2: 0.10829/Best R2: 0.10829: 2%|▏ | 84/5000 [00:03<03:08, 26.02it/s]
Current R2: 0.10832/Best R2: 0.10832: 2%|▏ | 84/5000 [00:03<03:08, 26.02it/s]
Current R2: 0.10835/Best R2: 0.10835: 2%|▏ | 84/5000 [00:03<03:08, 26.02it/s]
Current R2: 0.10835/Best R2: 0.10835: 2%|▏ | 87/5000 [00:03<03:07, 26.27it/s]
Current R2: 0.10838/Best R2: 0.10838: 2%|▏ | 87/5000 [00:03<03:07, 26.27it/s]
Current R2: 0.10841/Best R2: 0.10841: 2%|▏ | 87/5000 [00:03<03:07, 26.27it/s]
Current R2: 0.10844/Best R2: 0.10844: 2%|▏ | 87/5000 [00:03<03:07, 26.27it/s]
Current R2: 0.10844/Best R2: 0.10844: 2%|▏ | 90/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10847/Best R2: 0.10847: 2%|▏ | 90/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10850/Best R2: 0.10850: 2%|▏ | 90/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10853/Best R2: 0.10853: 2%|▏ | 90/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10853/Best R2: 0.10853: 2%|▏ | 93/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10855/Best R2: 0.10855: 2%|▏ | 93/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10858/Best R2: 0.10858: 2%|▏ | 93/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10861/Best R2: 0.10861: 2%|▏ | 93/5000 [00:03<03:06, 26.26it/s]
Current R2: 0.10861/Best R2: 0.10861: 2%|▏ | 96/5000 [00:03<03:06, 26.35it/s]
Current R2: 0.10863/Best R2: 0.10863: 2%|▏ | 96/5000 [00:03<03:06, 26.35it/s]
Current R2: 0.10866/Best R2: 0.10866: 2%|▏ | 96/5000 [00:03<03:06, 26.35it/s]
Current R2: 0.10868/Best R2: 0.10868: 2%|▏ | 96/5000 [00:03<03:06, 26.35it/s]
Current R2: 0.10868/Best R2: 0.10868: 2%|▏ | 99/5000 [00:03<03:06, 26.25it/s]
Current R2: 0.10871/Best R2: 0.10871: 2%|▏ | 99/5000 [00:03<03:06, 26.25it/s]
Current R2: 0.10873/Best R2: 0.10873: 2%|▏ | 99/5000 [00:03<03:06, 26.25it/s]
Current R2: 0.10876/Best R2: 0.10876: 2%|▏ | 99/5000 [00:03<03:06, 26.25it/s]
Current R2: 0.10876/Best R2: 0.10876: 2%|▏ | 102/5000 [00:03<03:07, 26.09it/s]
Current R2: 0.10878/Best R2: 0.10878: 2%|▏ | 102/5000 [00:04<03:07, 26.09it/s]
Current R2: 0.10880/Best R2: 0.10880: 2%|▏ | 102/5000 [00:04<03:07, 26.09it/s]
Current R2: 0.10883/Best R2: 0.10883: 2%|▏ | 102/5000 [00:04<03:07, 26.09it/s]
Current R2: 0.10883/Best R2: 0.10883: 2%|▏ | 105/5000 [00:04<03:07, 26.14it/s]
Current R2: 0.10885/Best R2: 0.10885: 2%|▏ | 105/5000 [00:04<03:07, 26.14it/s]
Current R2: 0.10887/Best R2: 0.10887: 2%|▏ | 105/5000 [00:04<03:07, 26.14it/s]
Current R2: 0.10889/Best R2: 0.10889: 2%|▏ | 105/5000 [00:04<03:07, 26.14it/s]
Current R2: 0.10889/Best R2: 0.10889: 2%|▏ | 108/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10892/Best R2: 0.10892: 2%|▏ | 108/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10894/Best R2: 0.10894: 2%|▏ | 108/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10896/Best R2: 0.10896: 2%|▏ | 108/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10896/Best R2: 0.10896: 2%|▏ | 111/5000 [00:04<03:04, 26.44it/s]
Current R2: 0.10898/Best R2: 0.10898: 2%|▏ | 111/5000 [00:04<03:04, 26.44it/s]
Current R2: 0.10900/Best R2: 0.10900: 2%|▏ | 111/5000 [00:04<03:04, 26.44it/s]
Current R2: 0.10902/Best R2: 0.10902: 2%|▏ | 111/5000 [00:04<03:04, 26.44it/s]
Current R2: 0.10902/Best R2: 0.10902: 2%|▏ | 114/5000 [00:04<03:04, 26.54it/s]
Current R2: 0.10904/Best R2: 0.10904: 2%|▏ | 114/5000 [00:04<03:04, 26.54it/s]
Current R2: 0.10906/Best R2: 0.10906: 2%|▏ | 114/5000 [00:04<03:04, 26.54it/s]
Current R2: 0.10908/Best R2: 0.10908: 2%|▏ | 114/5000 [00:04<03:04, 26.54it/s]
Current R2: 0.10908/Best R2: 0.10908: 2%|▏ | 117/5000 [00:04<03:05, 26.33it/s]
Current R2: 0.10910/Best R2: 0.10910: 2%|▏ | 117/5000 [00:04<03:05, 26.33it/s]
Current R2: 0.10912/Best R2: 0.10912: 2%|▏ | 117/5000 [00:04<03:05, 26.33it/s]
Current R2: 0.10914/Best R2: 0.10914: 2%|▏ | 117/5000 [00:04<03:05, 26.33it/s]
Current R2: 0.10914/Best R2: 0.10914: 2%|▏ | 120/5000 [00:04<03:03, 26.53it/s]
Current R2: 0.10916/Best R2: 0.10916: 2%|▏ | 120/5000 [00:04<03:03, 26.53it/s]
Current R2: 0.10917/Best R2: 0.10917: 2%|▏ | 120/5000 [00:04<03:03, 26.53it/s]
Current R2: 0.10919/Best R2: 0.10919: 2%|▏ | 120/5000 [00:04<03:03, 26.53it/s]
Current R2: 0.10919/Best R2: 0.10919: 2%|▏ | 123/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10921/Best R2: 0.10921: 2%|▏ | 123/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10923/Best R2: 0.10923: 2%|▏ | 123/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10924/Best R2: 0.10924: 2%|▏ | 123/5000 [00:04<03:05, 26.35it/s]
Current R2: 0.10924/Best R2: 0.10924: 3%|▎ | 126/5000 [00:04<03:15, 24.90it/s]
Current R2: 0.10926/Best R2: 0.10926: 3%|▎ | 126/5000 [00:04<03:15, 24.90it/s]
Current R2: 0.10928/Best R2: 0.10928: 3%|▎ | 126/5000 [00:05<03:15, 24.90it/s]
Current R2: 0.10929/Best R2: 0.10929: 3%|▎ | 126/5000 [00:05<03:15, 24.90it/s]
Current R2: 0.10929/Best R2: 0.10929: 3%|▎ | 129/5000 [00:05<03:13, 25.14it/s]
Current R2: 0.10931/Best R2: 0.10931: 3%|▎ | 129/5000 [00:05<03:13, 25.14it/s]
Current R2: 0.10933/Best R2: 0.10933: 3%|▎ | 129/5000 [00:05<03:13, 25.14it/s]
Current R2: 0.10934/Best R2: 0.10934: 3%|▎ | 129/5000 [00:05<03:13, 25.14it/s]
Current R2: 0.10934/Best R2: 0.10934: 3%|▎ | 132/5000 [00:05<03:11, 25.42it/s]
Current R2: 0.10936/Best R2: 0.10936: 3%|▎ | 132/5000 [00:05<03:11, 25.42it/s]
Current R2: 0.10937/Best R2: 0.10937: 3%|▎ | 132/5000 [00:05<03:11, 25.42it/s]
Current R2: 0.10939/Best R2: 0.10939: 3%|▎ | 132/5000 [00:05<03:11, 25.42it/s]
Current R2: 0.10939/Best R2: 0.10939: 3%|▎ | 135/5000 [00:05<03:10, 25.54it/s]
Current R2: 0.10941/Best R2: 0.10941: 3%|▎ | 135/5000 [00:05<03:10, 25.54it/s]
Current R2: 0.10942/Best R2: 0.10942: 3%|▎ | 135/5000 [00:05<03:10, 25.54it/s]
Current R2: 0.10944/Best R2: 0.10944: 3%|▎ | 135/5000 [00:05<03:10, 25.54it/s]
Current R2: 0.10944/Best R2: 0.10944: 3%|▎ | 138/5000 [00:05<03:09, 25.67it/s]
Current R2: 0.10945/Best R2: 0.10945: 3%|▎ | 138/5000 [00:05<03:09, 25.67it/s]
Current R2: 0.10946/Best R2: 0.10946: 3%|▎ | 138/5000 [00:05<03:09, 25.67it/s]
Current R2: 0.10948/Best R2: 0.10948: 3%|▎ | 138/5000 [00:05<03:09, 25.67it/s]
Current R2: 0.10948/Best R2: 0.10948: 3%|▎ | 141/5000 [00:05<03:08, 25.78it/s]
Current R2: 0.10949/Best R2: 0.10949: 3%|▎ | 141/5000 [00:05<03:08, 25.78it/s]
Current R2: 0.10951/Best R2: 0.10951: 3%|▎ | 141/5000 [00:05<03:08, 25.78it/s]
Current R2: 0.10952/Best R2: 0.10952: 3%|▎ | 141/5000 [00:05<03:08, 25.78it/s]
Current R2: 0.10952/Best R2: 0.10952: 3%|▎ | 144/5000 [00:05<03:09, 25.64it/s]
Current R2: 0.10953/Best R2: 0.10953: 3%|▎ | 144/5000 [00:05<03:09, 25.64it/s]
Current R2: 0.10955/Best R2: 0.10955: 3%|▎ | 144/5000 [00:05<03:09, 25.64it/s]
Current R2: 0.10956/Best R2: 0.10956: 3%|▎ | 144/5000 [00:05<03:09, 25.64it/s]
Current R2: 0.10956/Best R2: 0.10956: 3%|▎ | 147/5000 [00:05<03:07, 25.88it/s]
Current R2: 0.10958/Best R2: 0.10958: 3%|▎ | 147/5000 [00:05<03:07, 25.88it/s]
Current R2: 0.10959/Best R2: 0.10959: 3%|▎ | 147/5000 [00:05<03:07, 25.88it/s]
Current R2: 0.10960/Best R2: 0.10960: 3%|▎ | 147/5000 [00:05<03:07, 25.88it/s]
Current R2: 0.10960/Best R2: 0.10960: 3%|▎ | 150/5000 [00:05<03:09, 25.63it/s]
Current R2: 0.10961/Best R2: 0.10961: 3%|▎ | 150/5000 [00:05<03:09, 25.63it/s]
Current R2: 0.10963/Best R2: 0.10963: 3%|▎ | 150/5000 [00:05<03:09, 25.63it/s]
Current R2: 0.10964/Best R2: 0.10964: 3%|▎ | 150/5000 [00:05<03:09, 25.63it/s]
Current R2: 0.10964/Best R2: 0.10964: 3%|▎ | 153/5000 [00:05<03:11, 25.28it/s]
Current R2: 0.10965/Best R2: 0.10965: 3%|▎ | 153/5000 [00:06<03:11, 25.28it/s]
Current R2: 0.10966/Best R2: 0.10966: 3%|▎ | 153/5000 [00:06<03:11, 25.28it/s]
Current R2: 0.10968/Best R2: 0.10968: 3%|▎ | 153/5000 [00:06<03:11, 25.28it/s]
Current R2: 0.10968/Best R2: 0.10968: 3%|▎ | 156/5000 [00:06<03:10, 25.47it/s]
Current R2: 0.10969/Best R2: 0.10969: 3%|▎ | 156/5000 [00:06<03:10, 25.47it/s]
Current R2: 0.10970/Best R2: 0.10970: 3%|▎ | 156/5000 [00:06<03:10, 25.47it/s]
Current R2: 0.10971/Best R2: 0.10971: 3%|▎ | 156/5000 [00:06<03:10, 25.47it/s]
Current R2: 0.10971/Best R2: 0.10971: 3%|▎ | 159/5000 [00:06<03:07, 25.82it/s]
Current R2: 0.10972/Best R2: 0.10972: 3%|▎ | 159/5000 [00:06<03:07, 25.82it/s]
Current R2: 0.10974/Best R2: 0.10974: 3%|▎ | 159/5000 [00:06<03:07, 25.82it/s]
Current R2: 0.10975/Best R2: 0.10975: 3%|▎ | 159/5000 [00:06<03:07, 25.82it/s]
Current R2: 0.10975/Best R2: 0.10975: 3%|▎ | 162/5000 [00:06<03:07, 25.81it/s]
Current R2: 0.10976/Best R2: 0.10976: 3%|▎ | 162/5000 [00:06<03:07, 25.81it/s]
Current R2: 0.10977/Best R2: 0.10977: 3%|▎ | 162/5000 [00:06<03:07, 25.81it/s]
Current R2: 0.10978/Best R2: 0.10978: 3%|▎ | 162/5000 [00:06<03:07, 25.81it/s]
Current R2: 0.10978/Best R2: 0.10978: 3%|▎ | 165/5000 [00:06<03:07, 25.78it/s]
Current R2: 0.10979/Best R2: 0.10979: 3%|▎ | 165/5000 [00:06<03:07, 25.78it/s]
Current R2: 0.10980/Best R2: 0.10980: 3%|▎ | 165/5000 [00:06<03:07, 25.78it/s]
Current R2: 0.10981/Best R2: 0.10981: 3%|▎ | 165/5000 [00:06<03:07, 25.78it/s]
Current R2: 0.10981/Best R2: 0.10981: 3%|▎ | 168/5000 [00:06<03:12, 25.10it/s]
Current R2: 0.10982/Best R2: 0.10982: 3%|▎ | 168/5000 [00:06<03:12, 25.10it/s]
Current R2: 0.10983/Best R2: 0.10983: 3%|▎ | 168/5000 [00:06<03:12, 25.10it/s]
Current R2: 0.10985/Best R2: 0.10985: 3%|▎ | 168/5000 [00:06<03:12, 25.10it/s]
Current R2: 0.10985/Best R2: 0.10985: 3%|▎ | 171/5000 [00:06<03:08, 25.67it/s]
Current R2: 0.10986/Best R2: 0.10986: 3%|▎ | 171/5000 [00:06<03:08, 25.67it/s]
Current R2: 0.10987/Best R2: 0.10987: 3%|▎ | 171/5000 [00:06<03:08, 25.67it/s]
Current R2: 0.10988/Best R2: 0.10988: 3%|▎ | 171/5000 [00:06<03:08, 25.67it/s]
Current R2: 0.10988/Best R2: 0.10988: 3%|▎ | 174/5000 [00:06<03:07, 25.79it/s]
Current R2: 0.10989/Best R2: 0.10989: 3%|▎ | 174/5000 [00:06<03:07, 25.79it/s]
Current R2: 0.10990/Best R2: 0.10990: 3%|▎ | 174/5000 [00:06<03:07, 25.79it/s]
Current R2: 0.10991/Best R2: 0.10991: 3%|▎ | 174/5000 [00:06<03:07, 25.79it/s]
Current R2: 0.10991/Best R2: 0.10991: 4%|▎ | 177/5000 [00:06<03:05, 25.94it/s]
Current R2: 0.10992/Best R2: 0.10992: 4%|▎ | 177/5000 [00:06<03:05, 25.94it/s]
Current R2: 0.10993/Best R2: 0.10993: 4%|▎ | 177/5000 [00:06<03:05, 25.94it/s]
Current R2: 0.10994/Best R2: 0.10994: 4%|▎ | 177/5000 [00:07<03:05, 25.94it/s]
Current R2: 0.10994/Best R2: 0.10994: 4%|▎ | 180/5000 [00:07<03:06, 25.90it/s]
Current R2: 0.10994/Best R2: 0.10994: 4%|▎ | 180/5000 [00:07<03:06, 25.90it/s]
Current R2: 0.10995/Best R2: 0.10995: 4%|▎ | 180/5000 [00:07<03:06, 25.90it/s]
Current R2: 0.10996/Best R2: 0.10996: 4%|▎ | 180/5000 [00:07<03:06, 25.90it/s]
Current R2: 0.10996/Best R2: 0.10996: 4%|▎ | 183/5000 [00:07<03:05, 25.93it/s]
Current R2: 0.10997/Best R2: 0.10997: 4%|▎ | 183/5000 [00:07<03:05, 25.93it/s]
Current R2: 0.10998/Best R2: 0.10998: 4%|▎ | 183/5000 [00:07<03:05, 25.93it/s]
Current R2: 0.10999/Best R2: 0.10999: 4%|▎ | 183/5000 [00:07<03:05, 25.93it/s]
Current R2: 0.10999/Best R2: 0.10999: 4%|▎ | 186/5000 [00:07<03:06, 25.79it/s]
Current R2: 0.11000/Best R2: 0.11000: 4%|▎ | 186/5000 [00:07<03:06, 25.79it/s]
Current R2: 0.11001/Best R2: 0.11001: 4%|▎ | 186/5000 [00:07<03:06, 25.79it/s]
Current R2: 0.11002/Best R2: 0.11002: 4%|▎ | 186/5000 [00:07<03:06, 25.79it/s]
Current R2: 0.11002/Best R2: 0.11002: 4%|▍ | 189/5000 [00:07<03:17, 24.34it/s]
Current R2: 0.11003/Best R2: 0.11003: 4%|▍ | 189/5000 [00:07<03:17, 24.34it/s]
Current R2: 0.11004/Best R2: 0.11004: 4%|▍ | 189/5000 [00:07<03:17, 24.34it/s]
Current R2: 0.11004/Best R2: 0.11004: 4%|▍ | 189/5000 [00:07<03:17, 24.34it/s]
Current R2: 0.11004/Best R2: 0.11004: 4%|▍ | 192/5000 [00:07<03:13, 24.87it/s]
Current R2: 0.11005/Best R2: 0.11005: 4%|▍ | 192/5000 [00:07<03:13, 24.87it/s]
Current R2: 0.11006/Best R2: 0.11006: 4%|▍ | 192/5000 [00:07<03:13, 24.87it/s]
Current R2: 0.11007/Best R2: 0.11007: 4%|▍ | 192/5000 [00:07<03:13, 24.87it/s]
Current R2: 0.11007/Best R2: 0.11007: 4%|▍ | 195/5000 [00:07<03:09, 25.29it/s]
Current R2: 0.11008/Best R2: 0.11008: 4%|▍ | 195/5000 [00:07<03:09, 25.29it/s]
Current R2: 0.11009/Best R2: 0.11009: 4%|▍ | 195/5000 [00:07<03:09, 25.29it/s]
Current R2: 0.11009/Best R2: 0.11009: 4%|▍ | 195/5000 [00:07<03:09, 25.29it/s]
Current R2: 0.11009/Best R2: 0.11009: 4%|▍ | 198/5000 [00:07<03:07, 25.56it/s]
Current R2: 0.11010/Best R2: 0.11010: 4%|▍ | 198/5000 [00:07<03:07, 25.56it/s]
Current R2: 0.11011/Best R2: 0.11011: 4%|▍ | 198/5000 [00:07<03:07, 25.56it/s]
Current R2: 0.11012/Best R2: 0.11012: 4%|▍ | 198/5000 [00:07<03:07, 25.56it/s]
Current R2: 0.11012/Best R2: 0.11012: 4%|▍ | 201/5000 [00:07<03:08, 25.52it/s]
Current R2: 0.11013/Best R2: 0.11013: 4%|▍ | 201/5000 [00:07<03:08, 25.52it/s]
Current R2: 0.11013/Best R2: 0.11013: 4%|▍ | 201/5000 [00:07<03:08, 25.52it/s]
Current R2: 0.11014/Best R2: 0.11014: 4%|▍ | 201/5000 [00:07<03:08, 25.52it/s]
Current R2: 0.11014/Best R2: 0.11014: 4%|▍ | 204/5000 [00:07<03:05, 25.91it/s]
Current R2: 0.11015/Best R2: 0.11015: 4%|▍ | 204/5000 [00:08<03:05, 25.91it/s]
Current R2: 0.11016/Best R2: 0.11016: 4%|▍ | 204/5000 [00:08<03:05, 25.91it/s]
Current R2: 0.11017/Best R2: 0.11017: 4%|▍ | 204/5000 [00:08<03:05, 25.91it/s]
Current R2: 0.11017/Best R2: 0.11017: 4%|▍ | 207/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11017/Best R2: 0.11017: 4%|▍ | 207/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11018/Best R2: 0.11018: 4%|▍ | 207/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11019/Best R2: 0.11019: 4%|▍ | 207/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11019/Best R2: 0.11019: 4%|▍ | 210/5000 [00:08<03:02, 26.20it/s]
Current R2: 0.11020/Best R2: 0.11020: 4%|▍ | 210/5000 [00:08<03:02, 26.20it/s]
Current R2: 0.11020/Best R2: 0.11020: 4%|▍ | 210/5000 [00:08<03:02, 26.20it/s]
Current R2: 0.11021/Best R2: 0.11021: 4%|▍ | 210/5000 [00:08<03:02, 26.20it/s]
Current R2: 0.11021/Best R2: 0.11021: 4%|▍ | 213/5000 [00:08<03:03, 26.07it/s]
Current R2: 0.11022/Best R2: 0.11022: 4%|▍ | 213/5000 [00:08<03:03, 26.07it/s]
Current R2: 0.11022/Best R2: 0.11022: 4%|▍ | 213/5000 [00:08<03:03, 26.07it/s]
Current R2: 0.11023/Best R2: 0.11023: 4%|▍ | 213/5000 [00:08<03:03, 26.07it/s]
Current R2: 0.11023/Best R2: 0.11023: 4%|▍ | 216/5000 [00:08<03:03, 26.13it/s]
Current R2: 0.11024/Best R2: 0.11024: 4%|▍ | 216/5000 [00:08<03:03, 26.13it/s]
Current R2: 0.11025/Best R2: 0.11025: 4%|▍ | 216/5000 [00:08<03:03, 26.13it/s]
Current R2: 0.11025/Best R2: 0.11025: 4%|▍ | 216/5000 [00:08<03:03, 26.13it/s]
Current R2: 0.11025/Best R2: 0.11025: 4%|▍ | 219/5000 [00:08<03:03, 26.04it/s]
Current R2: 0.11026/Best R2: 0.11026: 4%|▍ | 219/5000 [00:08<03:03, 26.04it/s]
Current R2: 0.11027/Best R2: 0.11027: 4%|▍ | 219/5000 [00:08<03:03, 26.04it/s]
Current R2: 0.11027/Best R2: 0.11027: 4%|▍ | 219/5000 [00:08<03:03, 26.04it/s]
Current R2: 0.11027/Best R2: 0.11027: 4%|▍ | 222/5000 [00:08<03:04, 25.86it/s]
Current R2: 0.11028/Best R2: 0.11028: 4%|▍ | 222/5000 [00:08<03:04, 25.86it/s]
Current R2: 0.11029/Best R2: 0.11029: 4%|▍ | 222/5000 [00:08<03:04, 25.86it/s]
Current R2: 0.11029/Best R2: 0.11029: 4%|▍ | 222/5000 [00:08<03:04, 25.86it/s]
Current R2: 0.11029/Best R2: 0.11029: 4%|▍ | 225/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11030/Best R2: 0.11030: 4%|▍ | 225/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11031/Best R2: 0.11031: 4%|▍ | 225/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11031/Best R2: 0.11031: 4%|▍ | 225/5000 [00:08<03:03, 26.09it/s]
Current R2: 0.11031/Best R2: 0.11031: 5%|▍ | 228/5000 [00:08<03:01, 26.22it/s]
Current R2: 0.11032/Best R2: 0.11032: 5%|▍ | 228/5000 [00:08<03:01, 26.22it/s]
Current R2: 0.11033/Best R2: 0.11033: 5%|▍ | 228/5000 [00:08<03:01, 26.22it/s]
Current R2: 0.11033/Best R2: 0.11033: 5%|▍ | 228/5000 [00:09<03:01, 26.22it/s]
Current R2: 0.11033/Best R2: 0.11033: 5%|▍ | 231/5000 [00:09<03:02, 26.15it/s]
Current R2: 0.11034/Best R2: 0.11034: 5%|▍ | 231/5000 [00:09<03:02, 26.15it/s]
Current R2: 0.11034/Best R2: 0.11034: 5%|▍ | 231/5000 [00:09<03:02, 26.15it/s]
Current R2: 0.11035/Best R2: 0.11035: 5%|▍ | 231/5000 [00:09<03:02, 26.15it/s]
Current R2: 0.11035/Best R2: 0.11035: 5%|▍ | 234/5000 [00:09<03:00, 26.37it/s]
Current R2: 0.11036/Best R2: 0.11036: 5%|▍ | 234/5000 [00:09<03:00, 26.37it/s]
Current R2: 0.11036/Best R2: 0.11036: 5%|▍ | 234/5000 [00:09<03:00, 26.37it/s]
Current R2: 0.11037/Best R2: 0.11037: 5%|▍ | 234/5000 [00:09<03:00, 26.37it/s]
Current R2: 0.11037/Best R2: 0.11037: 5%|▍ | 237/5000 [00:09<03:01, 26.28it/s]
Current R2: 0.11038/Best R2: 0.11038: 5%|▍ | 237/5000 [00:09<03:01, 26.28it/s]
Current R2: 0.11038/Best R2: 0.11038: 5%|▍ | 237/5000 [00:09<03:01, 26.28it/s]
Current R2: 0.11039/Best R2: 0.11039: 5%|▍ | 237/5000 [00:09<03:01, 26.28it/s]
Current R2: 0.11039/Best R2: 0.11039: 5%|▍ | 240/5000 [00:09<03:01, 26.23it/s]
Current R2: 0.11039/Best R2: 0.11039: 5%|▍ | 240/5000 [00:09<03:01, 26.23it/s]
Current R2: 0.11040/Best R2: 0.11040: 5%|▍ | 240/5000 [00:09<03:01, 26.23it/s]
Current R2: 0.11041/Best R2: 0.11041: 5%|▍ | 240/5000 [00:09<03:01, 26.23it/s]
Current R2: 0.11041/Best R2: 0.11041: 5%|▍ | 243/5000 [00:09<03:03, 25.96it/s]
Current R2: 0.11041/Best R2: 0.11041: 5%|▍ | 243/5000 [00:09<03:03, 25.96it/s]
Current R2: 0.11042/Best R2: 0.11042: 5%|▍ | 243/5000 [00:09<03:03, 25.96it/s]
Current R2: 0.11042/Best R2: 0.11042: 5%|▍ | 243/5000 [00:09<03:03, 25.96it/s]
Current R2: 0.11042/Best R2: 0.11042: 5%|▍ | 246/5000 [00:09<03:03, 25.91it/s]
Current R2: 0.11043/Best R2: 0.11043: 5%|▍ | 246/5000 [00:09<03:03, 25.91it/s]
Current R2: 0.11043/Best R2: 0.11043: 5%|▍ | 246/5000 [00:09<03:03, 25.91it/s]
Current R2: 0.11044/Best R2: 0.11044: 5%|▍ | 246/5000 [00:09<03:03, 25.91it/s]
Current R2: 0.11044/Best R2: 0.11044: 5%|▍ | 249/5000 [00:09<03:03, 25.94it/s]
Current R2: 0.11045/Best R2: 0.11045: 5%|▍ | 249/5000 [00:09<03:03, 25.94it/s]
Current R2: 0.11045/Best R2: 0.11045: 5%|▍ | 249/5000 [00:09<03:03, 25.94it/s]
Current R2: 0.11046/Best R2: 0.11046: 5%|▍ | 249/5000 [00:09<03:03, 25.94it/s]
Current R2: 0.11046/Best R2: 0.11046: 5%|▌ | 252/5000 [00:09<03:02, 26.07it/s]
Current R2: 0.11046/Best R2: 0.11046: 5%|▌ | 252/5000 [00:09<03:02, 26.07it/s]
Current R2: 0.11047/Best R2: 0.11047: 5%|▌ | 252/5000 [00:09<03:02, 26.07it/s]
Current R2: 0.11047/Best R2: 0.11047: 5%|▌ | 252/5000 [00:09<03:02, 26.07it/s]
Current R2: 0.11047/Best R2: 0.11047: 5%|▌ | 255/5000 [00:09<03:02, 26.00it/s]
Current R2: 0.11048/Best R2: 0.11048: 5%|▌ | 255/5000 [00:09<03:02, 26.00it/s]
Current R2: 0.11048/Best R2: 0.11048: 5%|▌ | 255/5000 [00:10<03:02, 26.00it/s]
Current R2: 0.11049/Best R2: 0.11049: 5%|▌ | 255/5000 [00:10<03:02, 26.00it/s]
Current R2: 0.11049/Best R2: 0.11049: 5%|▌ | 258/5000 [00:10<03:03, 25.88it/s]
Current R2: 0.11049/Best R2: 0.11049: 5%|▌ | 258/5000 [00:10<03:03, 25.88it/s]
Current R2: 0.11050/Best R2: 0.11050: 5%|▌ | 258/5000 [00:10<03:03, 25.88it/s]
Current R2: 0.11050/Best R2: 0.11050: 5%|▌ | 258/5000 [00:10<03:03, 25.88it/s]
Current R2: 0.11050/Best R2: 0.11050: 5%|▌ | 261/5000 [00:10<03:01, 26.11it/s]
Current R2: 0.11051/Best R2: 0.11051: 5%|▌ | 261/5000 [00:10<03:01, 26.11it/s]
Current R2: 0.11051/Best R2: 0.11051: 5%|▌ | 261/5000 [00:10<03:01, 26.11it/s]
Current R2: 0.11052/Best R2: 0.11052: 5%|▌ | 261/5000 [00:10<03:01, 26.11it/s]
Current R2: 0.11052/Best R2: 0.11052: 5%|▌ | 264/5000 [00:10<03:02, 26.00it/s]
Current R2: 0.11053/Best R2: 0.11053: 5%|▌ | 264/5000 [00:10<03:02, 26.00it/s]
Current R2: 0.11053/Best R2: 0.11053: 5%|▌ | 264/5000 [00:10<03:02, 26.00it/s]
Current R2: 0.11054/Best R2: 0.11054: 5%|▌ | 264/5000 [00:10<03:02, 26.00it/s]
Current R2: 0.11054/Best R2: 0.11054: 5%|▌ | 267/5000 [00:10<03:03, 25.83it/s]
Current R2: 0.11054/Best R2: 0.11054: 5%|▌ | 267/5000 [00:10<03:03, 25.83it/s]
Current R2: 0.11055/Best R2: 0.11055: 5%|▌ | 267/5000 [00:10<03:03, 25.83it/s]
Current R2: 0.11055/Best R2: 0.11055: 5%|▌ | 267/5000 [00:10<03:03, 25.83it/s]
Current R2: 0.11055/Best R2: 0.11055: 5%|▌ | 270/5000 [00:10<03:03, 25.81it/s]
Current R2: 0.11055/Best R2: 0.11055: 5%|▌ | 270/5000 [00:10<03:03, 25.81it/s]
Current R2: 0.11056/Best R2: 0.11056: 5%|▌ | 270/5000 [00:10<03:03, 25.81it/s]
Current R2: 0.11056/Best R2: 0.11056: 5%|▌ | 270/5000 [00:10<03:03, 25.81it/s]
Current R2: 0.11056/Best R2: 0.11056: 5%|▌ | 273/5000 [00:10<03:03, 25.74it/s]
Current R2: 0.11057/Best R2: 0.11057: 5%|▌ | 273/5000 [00:10<03:03, 25.74it/s]
Current R2: 0.11057/Best R2: 0.11057: 5%|▌ | 273/5000 [00:10<03:03, 25.74it/s]
Current R2: 0.11058/Best R2: 0.11058: 5%|▌ | 273/5000 [00:10<03:03, 25.74it/s]
Current R2: 0.11058/Best R2: 0.11058: 6%|▌ | 276/5000 [00:10<03:04, 25.55it/s]
Current R2: 0.11058/Best R2: 0.11058: 6%|▌ | 276/5000 [00:10<03:04, 25.55it/s]
Current R2: 0.11059/Best R2: 0.11059: 6%|▌ | 276/5000 [00:10<03:04, 25.55it/s]
Current R2: 0.11059/Best R2: 0.11059: 6%|▌ | 276/5000 [00:10<03:04, 25.55it/s]
Current R2: 0.11059/Best R2: 0.11059: 6%|▌ | 279/5000 [00:10<03:03, 25.72it/s]
Current R2: 0.11060/Best R2: 0.11060: 6%|▌ | 279/5000 [00:10<03:03, 25.72it/s]
Current R2: 0.11060/Best R2: 0.11060: 6%|▌ | 279/5000 [00:10<03:03, 25.72it/s]
Current R2: 0.11061/Best R2: 0.11061: 6%|▌ | 279/5000 [00:10<03:03, 25.72it/s]
Current R2: 0.11061/Best R2: 0.11061: 6%|▌ | 282/5000 [00:10<03:02, 25.91it/s]
Current R2: 0.11061/Best R2: 0.11061: 6%|▌ | 282/5000 [00:11<03:02, 25.91it/s]
Current R2: 0.11062/Best R2: 0.11062: 6%|▌ | 282/5000 [00:11<03:02, 25.91it/s]
Current R2: 0.11062/Best R2: 0.11062: 6%|▌ | 282/5000 [00:11<03:02, 25.91it/s]
Current R2: 0.11062/Best R2: 0.11062: 6%|▌ | 285/5000 [00:11<03:02, 25.85it/s]
Current R2: 0.11062/Best R2: 0.11062: 6%|▌ | 285/5000 [00:11<03:02, 25.85it/s]
Current R2: 0.11063/Best R2: 0.11063: 6%|▌ | 285/5000 [00:11<03:02, 25.85it/s]
Current R2: 0.11063/Best R2: 0.11063: 6%|▌ | 285/5000 [00:11<03:02, 25.85it/s]
Current R2: 0.11063/Best R2: 0.11063: 6%|▌ | 288/5000 [00:11<03:03, 25.68it/s]
Current R2: 0.11064/Best R2: 0.11064: 6%|▌ | 288/5000 [00:11<03:03, 25.68it/s]
Current R2: 0.11064/Best R2: 0.11064: 6%|▌ | 288/5000 [00:11<03:03, 25.68it/s]
Current R2: 0.11065/Best R2: 0.11065: 6%|▌ | 288/5000 [00:11<03:03, 25.68it/s]
Current R2: 0.11065/Best R2: 0.11065: 6%|▌ | 291/5000 [00:11<03:03, 25.69it/s]
Current R2: 0.11065/Best R2: 0.11065: 6%|▌ | 291/5000 [00:11<03:03, 25.69it/s]
Current R2: 0.11066/Best R2: 0.11066: 6%|▌ | 291/5000 [00:11<03:03, 25.69it/s]
Current R2: 0.11066/Best R2: 0.11066: 6%|▌ | 291/5000 [00:11<03:03, 25.69it/s]
Current R2: 0.11066/Best R2: 0.11066: 6%|▌ | 294/5000 [00:11<03:02, 25.78it/s]
Current R2: 0.11066/Best R2: 0.11066: 6%|▌ | 294/5000 [00:11<03:02, 25.78it/s]
Current R2: 0.11067/Best R2: 0.11067: 6%|▌ | 294/5000 [00:11<03:02, 25.78it/s]
Current R2: 0.11067/Best R2: 0.11067: 6%|▌ | 294/5000 [00:11<03:02, 25.78it/s]
Current R2: 0.11067/Best R2: 0.11067: 6%|▌ | 297/5000 [00:11<03:01, 25.90it/s]
Current R2: 0.11068/Best R2: 0.11068: 6%|▌ | 297/5000 [00:11<03:01, 25.90it/s]
Current R2: 0.11068/Best R2: 0.11068: 6%|▌ | 297/5000 [00:11<03:01, 25.90it/s]
Current R2: 0.11068/Best R2: 0.11068: 6%|▌ | 297/5000 [00:11<03:01, 25.90it/s]
Current R2: 0.11068/Best R2: 0.11068: 6%|▌ | 300/5000 [00:11<03:02, 25.70it/s]
Current R2: 0.11069/Best R2: 0.11069: 6%|▌ | 300/5000 [00:11<03:02, 25.70it/s]
Current R2: 0.11069/Best R2: 0.11069: 6%|▌ | 300/5000 [00:11<03:02, 25.70it/s]
Current R2: 0.11070/Best R2: 0.11070: 6%|▌ | 300/5000 [00:11<03:02, 25.70it/s]
Current R2: 0.11070/Best R2: 0.11070: 6%|▌ | 303/5000 [00:11<03:08, 24.96it/s]
Current R2: 0.11070/Best R2: 0.11070: 6%|▌ | 303/5000 [00:11<03:08, 24.96it/s]
Current R2: 0.11071/Best R2: 0.11071: 6%|▌ | 303/5000 [00:11<03:08, 24.96it/s]
Current R2: 0.11071/Best R2: 0.11071: 6%|▌ | 303/5000 [00:11<03:08, 24.96it/s]
Current R2: 0.11071/Best R2: 0.11071: 6%|▌ | 306/5000 [00:11<03:05, 25.37it/s]
Current R2: 0.11071/Best R2: 0.11071: 6%|▌ | 306/5000 [00:11<03:05, 25.37it/s]
Current R2: 0.11072/Best R2: 0.11072: 6%|▌ | 306/5000 [00:11<03:05, 25.37it/s]
Current R2: 0.11072/Best R2: 0.11072: 6%|▌ | 306/5000 [00:12<03:05, 25.37it/s]
Current R2: 0.11072/Best R2: 0.11072: 6%|▌ | 309/5000 [00:12<03:05, 25.28it/s]
Current R2: 0.11072/Best R2: 0.11073: 6%|▌ | 309/5000 [00:12<03:05, 25.28it/s]
Current R2: 0.11073/Best R2: 0.11073: 6%|▌ | 309/5000 [00:12<03:05, 25.28it/s]
Current R2: 0.11073/Best R2: 0.11073: 6%|▌ | 309/5000 [00:12<03:05, 25.28it/s]
Current R2: 0.11073/Best R2: 0.11073: 6%|▌ | 312/5000 [00:12<03:07, 25.07it/s]
Current R2: 0.11074/Best R2: 0.11074: 6%|▌ | 312/5000 [00:12<03:07, 25.07it/s]
Current R2: 0.11074/Best R2: 0.11074: 6%|▌ | 312/5000 [00:12<03:07, 25.07it/s]
Current R2: 0.11074/Best R2: 0.11074: 6%|▌ | 312/5000 [00:12<03:07, 25.07it/s]
Current R2: 0.11074/Best R2: 0.11074: 6%|▋ | 315/5000 [00:12<03:06, 25.15it/s]
Current R2: 0.11075/Best R2: 0.11075: 6%|▋ | 315/5000 [00:12<03:06, 25.15it/s]
Current R2: 0.11075/Best R2: 0.11075: 6%|▋ | 315/5000 [00:12<03:06, 25.15it/s]
Current R2: 0.11076/Best R2: 0.11076: 6%|▋ | 315/5000 [00:12<03:06, 25.15it/s]
Current R2: 0.11076/Best R2: 0.11076: 6%|▋ | 318/5000 [00:12<03:05, 25.26it/s]
Current R2: 0.11076/Best R2: 0.11076: 6%|▋ | 318/5000 [00:12<03:05, 25.26it/s]
Current R2: 0.11076/Best R2: 0.11076: 6%|▋ | 318/5000 [00:12<03:05, 25.26it/s]
Current R2: 0.11077/Best R2: 0.11077: 6%|▋ | 318/5000 [00:12<03:05, 25.26it/s]
Current R2: 0.11077/Best R2: 0.11077: 6%|▋ | 321/5000 [00:12<03:04, 25.35it/s]
Current R2: 0.11077/Best R2: 0.11077: 6%|▋ | 321/5000 [00:12<03:04, 25.35it/s]
Current R2: 0.11077/Best R2: 0.11077: 6%|▋ | 321/5000 [00:12<03:04, 25.35it/s]
Current R2: 0.11078/Best R2: 0.11078: 6%|▋ | 321/5000 [00:12<03:04, 25.35it/s]
Current R2: 0.11078/Best R2: 0.11078: 6%|▋ | 324/5000 [00:12<03:05, 25.18it/s]
Current R2: 0.11078/Best R2: 0.11078: 6%|▋ | 324/5000 [00:12<03:05, 25.18it/s]
Current R2: 0.11078/Best R2: 0.11078: 6%|▋ | 324/5000 [00:12<03:05, 25.18it/s]
Current R2: 0.11079/Best R2: 0.11079: 6%|▋ | 324/5000 [00:12<03:05, 25.18it/s]
Current R2: 0.11079/Best R2: 0.11079: 7%|▋ | 327/5000 [00:12<03:05, 25.24it/s]
Current R2: 0.11079/Best R2: 0.11079: 7%|▋ | 327/5000 [00:12<03:05, 25.24it/s]
Current R2: 0.11080/Best R2: 0.11080: 7%|▋ | 327/5000 [00:12<03:05, 25.24it/s]
Current R2: 0.11080/Best R2: 0.11080: 7%|▋ | 327/5000 [00:12<03:05, 25.24it/s]
Current R2: 0.11080/Best R2: 0.11080: 7%|▋ | 330/5000 [00:12<03:04, 25.32it/s]
Current R2: 0.11080/Best R2: 0.11080: 7%|▋ | 330/5000 [00:12<03:04, 25.32it/s]
Current R2: 0.11081/Best R2: 0.11081: 7%|▋ | 330/5000 [00:12<03:04, 25.32it/s]
Current R2: 0.11081/Best R2: 0.11081: 7%|▋ | 330/5000 [00:12<03:04, 25.32it/s]
Current R2: 0.11081/Best R2: 0.11081: 7%|▋ | 333/5000 [00:12<03:02, 25.51it/s]
Current R2: 0.11081/Best R2: 0.11081: 7%|▋ | 333/5000 [00:13<03:02, 25.51it/s]
Current R2: 0.11082/Best R2: 0.11082: 7%|▋ | 333/5000 [00:13<03:02, 25.51it/s]
Current R2: 0.11082/Best R2: 0.11082: 7%|▋ | 333/5000 [00:13<03:02, 25.51it/s]
Current R2: 0.11082/Best R2: 0.11082: 7%|▋ | 336/5000 [00:13<03:19, 23.39it/s]
Current R2: 0.11082/Best R2: 0.11082: 7%|▋ | 336/5000 [00:13<03:19, 23.39it/s]
Current R2: 0.11083/Best R2: 0.11083: 7%|▋ | 336/5000 [00:13<03:19, 23.39it/s]
Current R2: 0.11083/Best R2: 0.11083: 7%|▋ | 336/5000 [00:13<03:19, 23.39it/s]
Current R2: 0.11083/Best R2: 0.11083: 7%|▋ | 339/5000 [00:13<03:15, 23.83it/s]
Current R2: 0.11083/Best R2: 0.11083: 7%|▋ | 339/5000 [00:13<03:15, 23.83it/s]
Current R2: 0.11084/Best R2: 0.11084: 7%|▋ | 339/5000 [00:13<03:15, 23.83it/s]
Current R2: 0.11084/Best R2: 0.11084: 7%|▋ | 339/5000 [00:13<03:15, 23.83it/s]
Current R2: 0.11084/Best R2: 0.11084: 7%|▋ | 342/5000 [00:13<03:12, 24.17it/s]
Current R2: 0.11084/Best R2: 0.11084: 7%|▋ | 342/5000 [00:13<03:12, 24.17it/s]
Current R2: 0.11085/Best R2: 0.11085: 7%|▋ | 342/5000 [00:13<03:12, 24.17it/s]
Current R2: 0.11085/Best R2: 0.11085: 7%|▋ | 342/5000 [00:13<03:12, 24.17it/s]
Current R2: 0.11085/Best R2: 0.11085: 7%|▋ | 345/5000 [00:13<03:10, 24.41it/s]
Current R2: 0.11085/Best R2: 0.11085: 7%|▋ | 345/5000 [00:13<03:10, 24.41it/s]
Current R2: 0.11086/Best R2: 0.11086: 7%|▋ | 345/5000 [00:13<03:10, 24.41it/s]
Current R2: 0.11086/Best R2: 0.11086: 7%|▋ | 345/5000 [00:13<03:10, 24.41it/s]
Current R2: 0.11086/Best R2: 0.11086: 7%|▋ | 348/5000 [00:13<03:09, 24.54it/s]
Current R2: 0.11086/Best R2: 0.11086: 7%|▋ | 348/5000 [00:13<03:09, 24.54it/s]
Current R2: 0.11087/Best R2: 0.11087: 7%|▋ | 348/5000 [00:13<03:09, 24.54it/s]
Current R2: 0.11087/Best R2: 0.11087: 7%|▋ | 348/5000 [00:13<03:09, 24.54it/s]
Current R2: 0.11087/Best R2: 0.11087: 7%|▋ | 351/5000 [00:13<03:07, 24.74it/s]
Current R2: 0.11087/Best R2: 0.11087: 7%|▋ | 351/5000 [00:13<03:07, 24.74it/s]
Current R2: 0.11088/Best R2: 0.11088: 7%|▋ | 351/5000 [00:13<03:07, 24.74it/s]
Current R2: 0.11088/Best R2: 0.11088: 7%|▋ | 351/5000 [00:13<03:07, 24.74it/s]
Current R2: 0.11088/Best R2: 0.11088: 7%|▋ | 354/5000 [00:13<03:06, 24.85it/s]
Current R2: 0.11088/Best R2: 0.11088: 7%|▋ | 354/5000 [00:13<03:06, 24.85it/s]
Current R2: 0.11088/Best R2: 0.11088: 7%|▋ | 354/5000 [00:13<03:06, 24.85it/s]
Current R2: 0.11089/Best R2: 0.11089: 7%|▋ | 354/5000 [00:13<03:06, 24.85it/s]
Current R2: 0.11089/Best R2: 0.11089: 7%|▋ | 357/5000 [00:13<03:04, 25.14it/s]
Current R2: 0.11089/Best R2: 0.11089: 7%|▋ | 357/5000 [00:14<03:04, 25.14it/s]
Current R2: 0.11089/Best R2: 0.11089: 7%|▋ | 357/5000 [00:14<03:04, 25.14it/s]
Current R2: 0.11090/Best R2: 0.11090: 7%|▋ | 357/5000 [00:14<03:04, 25.14it/s]
Current R2: 0.11090/Best R2: 0.11090: 7%|▋ | 360/5000 [00:14<03:03, 25.27it/s]
Current R2: 0.11090/Best R2: 0.11090: 7%|▋ | 360/5000 [00:14<03:03, 25.27it/s]
Current R2: 0.11090/Best R2: 0.11090: 7%|▋ | 360/5000 [00:14<03:03, 25.27it/s]
Current R2: 0.11091/Best R2: 0.11091: 7%|▋ | 360/5000 [00:14<03:03, 25.27it/s]
Current R2: 0.11091/Best R2: 0.11091: 7%|▋ | 363/5000 [00:14<03:04, 25.12it/s]
Current R2: 0.11091/Best R2: 0.11091: 7%|▋ | 363/5000 [00:14<03:04, 25.12it/s]
Current R2: 0.11091/Best R2: 0.11091: 7%|▋ | 363/5000 [00:14<03:04, 25.12it/s]
Current R2: 0.11091/Best R2: 0.11091: 7%|▋ | 363/5000 [00:14<03:04, 25.12it/s]
Current R2: 0.11091/Best R2: 0.11091: 7%|▋ | 366/5000 [00:14<03:06, 24.91it/s]
Current R2: 0.11092/Best R2: 0.11092: 7%|▋ | 366/5000 [00:14<03:06, 24.91it/s]
Current R2: 0.11092/Best R2: 0.11092: 7%|▋ | 366/5000 [00:14<03:06, 24.91it/s]
Current R2: 0.11092/Best R2: 0.11092: 7%|▋ | 366/5000 [00:14<03:06, 24.91it/s]
Current R2: 0.11092/Best R2: 0.11092: 7%|▋ | 369/5000 [00:14<03:04, 25.07it/s]
Current R2: 0.11093/Best R2: 0.11093: 7%|▋ | 369/5000 [00:14<03:04, 25.07it/s]
Current R2: 0.11093/Best R2: 0.11093: 7%|▋ | 369/5000 [00:14<03:04, 25.07it/s]
Current R2: 0.11093/Best R2: 0.11093: 7%|▋ | 369/5000 [00:14<03:04, 25.07it/s]
Current R2: 0.11093/Best R2: 0.11093: 7%|▋ | 372/5000 [00:14<03:04, 25.02it/s]
Current R2: 0.11093/Best R2: 0.11093: 7%|▋ | 372/5000 [00:14<03:04, 25.02it/s]
Current R2: 0.11094/Best R2: 0.11094: 7%|▋ | 372/5000 [00:14<03:04, 25.02it/s]
Current R2: 0.11094/Best R2: 0.11094: 7%|▋ | 372/5000 [00:14<03:04, 25.02it/s]
Current R2: 0.11094/Best R2: 0.11094: 8%|▊ | 375/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11094/Best R2: 0.11094: 8%|▊ | 375/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11095/Best R2: 0.11095: 8%|▊ | 375/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11095/Best R2: 0.11095: 8%|▊ | 375/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11095/Best R2: 0.11095: 8%|▊ | 378/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11095/Best R2: 0.11095: 8%|▊ | 378/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11095/Best R2: 0.11095: 8%|▊ | 378/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11096/Best R2: 0.11096: 8%|▊ | 378/5000 [00:14<03:04, 25.01it/s]
Current R2: 0.11096/Best R2: 0.11096: 8%|▊ | 381/5000 [00:14<03:11, 24.17it/s]
Current R2: 0.11096/Best R2: 0.11096: 8%|▊ | 381/5000 [00:14<03:11, 24.17it/s]
Current R2: 0.11096/Best R2: 0.11096: 8%|▊ | 381/5000 [00:15<03:11, 24.17it/s]
Current R2: 0.11097/Best R2: 0.11097: 8%|▊ | 381/5000 [00:15<03:11, 24.17it/s]
Current R2: 0.11097/Best R2: 0.11097: 8%|▊ | 384/5000 [00:15<03:44, 20.52it/s]
Current R2: 0.11097/Best R2: 0.11097: 8%|▊ | 384/5000 [00:15<03:44, 20.52it/s]
Current R2: 0.11097/Best R2: 0.11097: 8%|▊ | 384/5000 [00:15<03:44, 20.52it/s]
Current R2: 0.11097/Best R2: 0.11097: 8%|▊ | 384/5000 [00:15<03:44, 20.52it/s]
Current R2: 0.11097/Best R2: 0.11097: 8%|▊ | 387/5000 [00:15<03:53, 19.79it/s]
Current R2: 0.11098/Best R2: 0.11098: 8%|▊ | 387/5000 [00:15<03:53, 19.79it/s]
Current R2: 0.11098/Best R2: 0.11098: 8%|▊ | 387/5000 [00:15<03:53, 19.79it/s]
Current R2: 0.11098/Best R2: 0.11098: 8%|▊ | 387/5000 [00:15<03:53, 19.79it/s]
Current R2: 0.11098/Best R2: 0.11098: 8%|▊ | 390/5000 [00:15<03:36, 21.27it/s]
Current R2: 0.11098/Best R2: 0.11098: 8%|▊ | 390/5000 [00:15<03:36, 21.27it/s]
Current R2: 0.11099/Best R2: 0.11099: 8%|▊ | 390/5000 [00:15<03:36, 21.27it/s]
Current R2: 0.11099/Best R2: 0.11099: 8%|▊ | 390/5000 [00:15<03:36, 21.27it/s]
Current R2: 0.11099/Best R2: 0.11099: 8%|▊ | 393/5000 [00:15<03:41, 20.79it/s]
Current R2: 0.11099/Best R2: 0.11099: 8%|▊ | 393/5000 [00:15<03:41, 20.79it/s]
Current R2: 0.11099/Best R2: 0.11099: 8%|▊ | 393/5000 [00:15<03:41, 20.79it/s]
Current R2: 0.11100/Best R2: 0.11100: 8%|▊ | 393/5000 [00:15<03:41, 20.79it/s]
Current R2: 0.11100/Best R2: 0.11100: 8%|▊ | 396/5000 [00:15<04:19, 17.73it/s]
Current R2: 0.11100/Best R2: 0.11100: 8%|▊ | 396/5000 [00:15<04:19, 17.73it/s]
Current R2: 0.11100/Best R2: 0.11100: 8%|▊ | 396/5000 [00:15<04:19, 17.73it/s]
Current R2: 0.11100/Best R2: 0.11100: 8%|▊ | 398/5000 [00:15<04:34, 16.75it/s]
Current R2: 0.11100/Best R2: 0.11100: 8%|▊ | 398/5000 [00:16<04:34, 16.75it/s]
Current R2: 0.11101/Best R2: 0.11101: 8%|▊ | 398/5000 [00:16<04:34, 16.75it/s]
Current R2: 0.11101/Best R2: 0.11101: 8%|▊ | 400/5000 [00:16<05:41, 13.46it/s]
Current R2: 0.11101/Best R2: 0.11101: 8%|▊ | 400/5000 [00:16<05:41, 13.46it/s]
Current R2: 0.11101/Best R2: 0.11101: 8%|▊ | 400/5000 [00:16<05:41, 13.46it/s]
Current R2: 0.11101/Best R2: 0.11101: 8%|▊ | 400/5000 [00:16<05:41, 13.46it/s]
Current R2: 0.11101/Best R2: 0.11101: 8%|▊ | 403/5000 [00:16<04:52, 15.73it/s]
Current R2: 0.11102/Best R2: 0.11102: 8%|▊ | 403/5000 [00:16<04:52, 15.73it/s]
Current R2: 0.11102/Best R2: 0.11102: 8%|▊ | 403/5000 [00:16<04:52, 15.73it/s]
Current R2: 0.11102/Best R2: 0.11102: 8%|▊ | 403/5000 [00:16<04:52, 15.73it/s]
Current R2: 0.11102/Best R2: 0.11102: 8%|▊ | 406/5000 [00:16<04:17, 17.84it/s]
Current R2: 0.11102/Best R2: 0.11102: 8%|▊ | 406/5000 [00:16<04:17, 17.84it/s]
Current R2: 0.11103/Best R2: 0.11103: 8%|▊ | 406/5000 [00:16<04:17, 17.84it/s]
Current R2: 0.11103/Best R2: 0.11103: 8%|▊ | 406/5000 [00:16<04:17, 17.84it/s]
Current R2: 0.11103/Best R2: 0.11103: 8%|▊ | 409/5000 [00:16<03:53, 19.66it/s]
Current R2: 0.11103/Best R2: 0.11103: 8%|▊ | 409/5000 [00:16<03:53, 19.66it/s]
Current R2: 0.11103/Best R2: 0.11103: 8%|▊ | 409/5000 [00:16<03:53, 19.66it/s]
Current R2: 0.11104/Best R2: 0.11104: 8%|▊ | 409/5000 [00:16<03:53, 19.66it/s]
Current R2: 0.11104/Best R2: 0.11104: 8%|▊ | 412/5000 [00:16<03:37, 21.08it/s]
Current R2: 0.11104/Best R2: 0.11104: 8%|▊ | 412/5000 [00:16<03:37, 21.08it/s]
Current R2: 0.11104/Best R2: 0.11104: 8%|▊ | 412/5000 [00:16<03:37, 21.08it/s]
Current R2: 0.11104/Best R2: 0.11104: 8%|▊ | 412/5000 [00:16<03:37, 21.08it/s]
Current R2: 0.11104/Best R2: 0.11104: 8%|▊ | 415/5000 [00:16<03:27, 22.05it/s]
Current R2: 0.11105/Best R2: 0.11105: 8%|▊ | 415/5000 [00:16<03:27, 22.05it/s]
Current R2: 0.11105/Best R2: 0.11105: 8%|▊ | 415/5000 [00:16<03:27, 22.05it/s]
Current R2: 0.11105/Best R2: 0.11105: 8%|▊ | 415/5000 [00:16<03:27, 22.05it/s]
Current R2: 0.11105/Best R2: 0.11105: 8%|▊ | 418/5000 [00:16<03:20, 22.86it/s]
Current R2: 0.11105/Best R2: 0.11105: 8%|▊ | 418/5000 [00:16<03:20, 22.86it/s]
Current R2: 0.11105/Best R2: 0.11105: 8%|▊ | 418/5000 [00:17<03:20, 22.86it/s]
Current R2: 0.11106/Best R2: 0.11106: 8%|▊ | 418/5000 [00:17<03:20, 22.86it/s]
Current R2: 0.11106/Best R2: 0.11106: 8%|▊ | 421/5000 [00:17<03:24, 22.37it/s]
Current R2: 0.11106/Best R2: 0.11106: 8%|▊ | 421/5000 [00:17<03:24, 22.37it/s]
Current R2: 0.11106/Best R2: 0.11106: 8%|▊ | 421/5000 [00:17<03:24, 22.37it/s]
Current R2: 0.11106/Best R2: 0.11106: 8%|▊ | 421/5000 [00:17<03:24, 22.37it/s]
Current R2: 0.11106/Best R2: 0.11106: 8%|▊ | 424/5000 [00:17<03:18, 23.06it/s]
Current R2: 0.11107/Best R2: 0.11107: 8%|▊ | 424/5000 [00:17<03:18, 23.06it/s]
Current R2: 0.11107/Best R2: 0.11107: 8%|▊ | 424/5000 [00:17<03:18, 23.06it/s]
Current R2: 0.11107/Best R2: 0.11107: 8%|▊ | 424/5000 [00:17<03:18, 23.06it/s]
Current R2: 0.11107/Best R2: 0.11107: 9%|▊ | 427/5000 [00:17<03:12, 23.71it/s]
Current R2: 0.11107/Best R2: 0.11107: 9%|▊ | 427/5000 [00:17<03:12, 23.71it/s]
Current R2: 0.11107/Best R2: 0.11107: 9%|▊ | 427/5000 [00:17<03:12, 23.71it/s]
Current R2: 0.11108/Best R2: 0.11108: 9%|▊ | 427/5000 [00:17<03:12, 23.71it/s]
Current R2: 0.11108/Best R2: 0.11108: 9%|▊ | 430/5000 [00:17<03:06, 24.50it/s]
Current R2: 0.11108/Best R2: 0.11108: 9%|▊ | 430/5000 [00:17<03:06, 24.50it/s]
Current R2: 0.11108/Best R2: 0.11108: 9%|▊ | 430/5000 [00:17<03:06, 24.50it/s]
Current R2: 0.11108/Best R2: 0.11108: 9%|▊ | 430/5000 [00:17<03:06, 24.50it/s]
Current R2: 0.11108/Best R2: 0.11108: 9%|▊ | 433/5000 [00:17<03:02, 24.97it/s]
Current R2: 0.11109/Best R2: 0.11109: 9%|▊ | 433/5000 [00:17<03:02, 24.97it/s]
Current R2: 0.11109/Best R2: 0.11109: 9%|▊ | 433/5000 [00:17<03:02, 24.97it/s]
Current R2: 0.11109/Best R2: 0.11109: 9%|▊ | 433/5000 [00:17<03:02, 24.97it/s]
Current R2: 0.11109/Best R2: 0.11109: 9%|▊ | 436/5000 [00:17<03:01, 25.13it/s]
Current R2: 0.11109/Best R2: 0.11109: 9%|▊ | 436/5000 [00:17<03:01, 25.13it/s]
Current R2: 0.11109/Best R2: 0.11109: 9%|▊ | 436/5000 [00:17<03:01, 25.13it/s]
Current R2: 0.11110/Best R2: 0.11110: 9%|▊ | 436/5000 [00:17<03:01, 25.13it/s]
Current R2: 0.11110/Best R2: 0.11110: 9%|▉ | 439/5000 [00:17<03:01, 25.08it/s]
Current R2: 0.11110/Best R2: 0.11110: 9%|▉ | 439/5000 [00:17<03:01, 25.08it/s]
Current R2: 0.11110/Best R2: 0.11110: 9%|▉ | 439/5000 [00:17<03:01, 25.08it/s]
Current R2: 0.11110/Best R2: 0.11110: 9%|▉ | 439/5000 [00:17<03:01, 25.08it/s]
Current R2: 0.11110/Best R2: 0.11110: 9%|▉ | 442/5000 [00:17<03:03, 24.79it/s]
Current R2: 0.11110/Best R2: 0.11110: 9%|▉ | 442/5000 [00:17<03:03, 24.79it/s]
Current R2: 0.11111/Best R2: 0.11111: 9%|▉ | 442/5000 [00:17<03:03, 24.79it/s]
Current R2: 0.11111/Best R2: 0.11111: 9%|▉ | 442/5000 [00:18<03:03, 24.79it/s]
Current R2: 0.11111/Best R2: 0.11111: 9%|▉ | 445/5000 [00:18<03:03, 24.79it/s]
Current R2: 0.11111/Best R2: 0.11111: 9%|▉ | 445/5000 [00:18<03:03, 24.79it/s]
Current R2: 0.11111/Best R2: 0.11111: 9%|▉ | 445/5000 [00:18<03:03, 24.79it/s]
Current R2: 0.11112/Best R2: 0.11112: 9%|▉ | 445/5000 [00:18<03:03, 24.79it/s]
Current R2: 0.11112/Best R2: 0.11112: 9%|▉ | 448/5000 [00:18<03:04, 24.73it/s]
Current R2: 0.11112/Best R2: 0.11112: 9%|▉ | 448/5000 [00:18<03:04, 24.73it/s]
Current R2: 0.11112/Best R2: 0.11112: 9%|▉ | 448/5000 [00:18<03:04, 24.73it/s]
Current R2: 0.11112/Best R2: 0.11112: 9%|▉ | 448/5000 [00:18<03:04, 24.73it/s]
Current R2: 0.11112/Best R2: 0.11112: 9%|▉ | 451/5000 [00:18<03:03, 24.81it/s]
Current R2: 0.11112/Best R2: 0.11112: 9%|▉ | 451/5000 [00:18<03:03, 24.81it/s]
Current R2: 0.11113/Best R2: 0.11113: 9%|▉ | 451/5000 [00:18<03:03, 24.81it/s]
Current R2: 0.11113/Best R2: 0.11113: 9%|▉ | 451/5000 [00:18<03:03, 24.81it/s]
Current R2: 0.11113/Best R2: 0.11113: 9%|▉ | 454/5000 [00:18<03:02, 24.86it/s]
Current R2: 0.11113/Best R2: 0.11113: 9%|▉ | 454/5000 [00:18<03:02, 24.86it/s]
Current R2: 0.11113/Best R2: 0.11113: 9%|▉ | 454/5000 [00:18<03:02, 24.86it/s]
Current R2: 0.11113/Best R2: 0.11113: 9%|▉ | 454/5000 [00:18<03:02, 24.86it/s]
Current R2: 0.11113/Best R2: 0.11113: 9%|▉ | 457/5000 [00:18<03:01, 25.06it/s]
Current R2: 0.11113/Best R2: 0.11114: 9%|▉ | 457/5000 [00:18<03:01, 25.06it/s]
Current R2: 0.11114/Best R2: 0.11114: 9%|▉ | 457/5000 [00:18<03:01, 25.06it/s]
Current R2: 0.11114/Best R2: 0.11114: 9%|▉ | 457/5000 [00:18<03:01, 25.06it/s]
Current R2: 0.11114/Best R2: 0.11114: 9%|▉ | 460/5000 [00:18<02:59, 25.24it/s]
Current R2: 0.11114/Best R2: 0.11114: 9%|▉ | 460/5000 [00:18<02:59, 25.24it/s]
Current R2: 0.11114/Best R2: 0.11114: 9%|▉ | 460/5000 [00:18<02:59, 25.24it/s]
Current R2: 0.11114/Best R2: 0.11114: 9%|▉ | 460/5000 [00:18<02:59, 25.24it/s]
Current R2: 0.11114/Best R2: 0.11114: 9%|▉ | 463/5000 [00:18<02:59, 25.22it/s]
Current R2: 0.11115/Best R2: 0.11115: 9%|▉ | 463/5000 [00:18<02:59, 25.22it/s]
Current R2: 0.11115/Best R2: 0.11115: 9%|▉ | 463/5000 [00:18<02:59, 25.22it/s]
Current R2: 0.11115/Best R2: 0.11115: 9%|▉ | 463/5000 [00:18<02:59, 25.22it/s]
Current R2: 0.11115/Best R2: 0.11115: 9%|▉ | 466/5000 [00:18<03:00, 25.19it/s]
Current R2: 0.11115/Best R2: 0.11115: 9%|▉ | 466/5000 [00:18<03:00, 25.19it/s]
Current R2: 0.11115/Best R2: 0.11115: 9%|▉ | 466/5000 [00:18<03:00, 25.19it/s]
Current R2: 0.11116/Best R2: 0.11116: 9%|▉ | 466/5000 [00:18<03:00, 25.19it/s]
Current R2: 0.11116/Best R2: 0.11116: 9%|▉ | 469/5000 [00:18<02:59, 25.25it/s]
Current R2: 0.11116/Best R2: 0.11116: 9%|▉ | 469/5000 [00:18<02:59, 25.25it/s]
Current R2: 0.11116/Best R2: 0.11116: 9%|▉ | 469/5000 [00:19<02:59, 25.25it/s]
Current R2: 0.11116/Best R2: 0.11116: 9%|▉ | 469/5000 [00:19<02:59, 25.25it/s]
Current R2: 0.11116/Best R2: 0.11116: 9%|▉ | 472/5000 [00:19<02:59, 25.18it/s]
Current R2: 0.11116/Best R2: 0.11116: 9%|▉ | 472/5000 [00:19<02:59, 25.18it/s]
Current R2: 0.11117/Best R2: 0.11117: 9%|▉ | 472/5000 [00:19<02:59, 25.18it/s]
Current R2: 0.11117/Best R2: 0.11117: 9%|▉ | 472/5000 [00:19<02:59, 25.18it/s]
Current R2: 0.11117/Best R2: 0.11117: 10%|▉ | 475/5000 [00:19<03:01, 24.89it/s]
Current R2: 0.11117/Best R2: 0.11117: 10%|▉ | 475/5000 [00:19<03:01, 24.89it/s]
Current R2: 0.11117/Best R2: 0.11117: 10%|▉ | 475/5000 [00:19<03:01, 24.89it/s]
Current R2: 0.11117/Best R2: 0.11117: 10%|▉ | 475/5000 [00:19<03:01, 24.89it/s]
Current R2: 0.11117/Best R2: 0.11117: 10%|▉ | 478/5000 [00:19<03:01, 24.97it/s]
Current R2: 0.11117/Best R2: 0.11117: 10%|▉ | 478/5000 [00:19<03:01, 24.97it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 478/5000 [00:19<03:01, 24.97it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 478/5000 [00:19<03:01, 24.97it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 481/5000 [00:19<02:59, 25.16it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 481/5000 [00:19<02:59, 25.16it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 481/5000 [00:19<02:59, 25.16it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 481/5000 [00:19<02:59, 25.16it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 484/5000 [00:19<02:57, 25.40it/s]
Current R2: 0.11118/Best R2: 0.11118: 10%|▉ | 484/5000 [00:19<02:57, 25.40it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 484/5000 [00:19<02:57, 25.40it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 484/5000 [00:19<02:57, 25.40it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 487/5000 [00:19<02:56, 25.59it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 487/5000 [00:19<02:56, 25.59it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 487/5000 [00:19<02:56, 25.59it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 487/5000 [00:19<02:56, 25.59it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 490/5000 [00:19<02:58, 25.28it/s]
Current R2: 0.11119/Best R2: 0.11119: 10%|▉ | 490/5000 [00:19<02:58, 25.28it/s]
Current R2: 0.11120/Best R2: 0.11120: 10%|▉ | 490/5000 [00:19<02:58, 25.28it/s]
Current R2: 0.11120/Best R2: 0.11120: 10%|▉ | 490/5000 [00:19<02:58, 25.28it/s]
Current R2: 0.11120/Best R2: 0.11120: 10%|▉ | 493/5000 [00:19<03:04, 24.36it/s]
Current R2: 0.11120/Best R2: 0.11120: 10%|▉ | 493/5000 [00:19<03:04, 24.36it/s]
Current R2: 0.11120/Best R2: 0.11120: 10%|▉ | 493/5000 [00:20<03:04, 24.36it/s]
Current R2: 0.11120/Best R2: 0.11120: 10%|▉ | 493/5000 [00:20<03:04, 24.36it/s]
Current R2: 0.11120/Best R2: 0.11120: 10%|▉ | 496/5000 [00:20<03:02, 24.62it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|▉ | 496/5000 [00:20<03:02, 24.62it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|▉ | 496/5000 [00:20<03:02, 24.62it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|▉ | 496/5000 [00:20<03:02, 24.62it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|▉ | 499/5000 [00:20<03:01, 24.84it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|▉ | 499/5000 [00:20<03:01, 24.84it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|▉ | 499/5000 [00:20<03:01, 24.84it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|▉ | 499/5000 [00:20<03:01, 24.84it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|█ | 502/5000 [00:20<02:59, 24.99it/s]
Current R2: 0.11121/Best R2: 0.11121: 10%|█ | 502/5000 [00:20<02:59, 24.99it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 502/5000 [00:20<02:59, 24.99it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 502/5000 [00:20<02:59, 24.99it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 505/5000 [00:20<02:59, 25.04it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 505/5000 [00:20<02:59, 25.04it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 505/5000 [00:20<02:59, 25.04it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 505/5000 [00:20<02:59, 25.04it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 508/5000 [00:20<03:07, 23.96it/s]
Current R2: 0.11122/Best R2: 0.11122: 10%|█ | 508/5000 [00:20<03:07, 23.96it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 508/5000 [00:20<03:07, 23.96it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 508/5000 [00:20<03:07, 23.96it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 511/5000 [00:20<03:05, 24.14it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 511/5000 [00:20<03:05, 24.14it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 511/5000 [00:20<03:05, 24.14it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 511/5000 [00:20<03:05, 24.14it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 514/5000 [00:20<03:04, 24.30it/s]
Current R2: 0.11123/Best R2: 0.11123: 10%|█ | 514/5000 [00:20<03:04, 24.30it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 514/5000 [00:20<03:04, 24.30it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 514/5000 [00:20<03:04, 24.30it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 517/5000 [00:20<03:03, 24.47it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 517/5000 [00:20<03:03, 24.47it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 517/5000 [00:20<03:03, 24.47it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 517/5000 [00:21<03:03, 24.47it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 520/5000 [00:21<03:01, 24.62it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 520/5000 [00:21<03:01, 24.62it/s]
Current R2: 0.11124/Best R2: 0.11124: 10%|█ | 520/5000 [00:21<03:01, 24.62it/s]
Current R2: 0.11125/Best R2: 0.11125: 10%|█ | 520/5000 [00:21<03:01, 24.62it/s]
Current R2: 0.11125/Best R2: 0.11125: 10%|█ | 523/5000 [00:21<03:00, 24.78it/s]
Current R2: 0.11125/Best R2: 0.11125: 10%|█ | 523/5000 [00:21<03:00, 24.78it/s]
Current R2: 0.11125/Best R2: 0.11125: 10%|█ | 523/5000 [00:21<03:00, 24.78it/s]
Current R2: 0.11125/Best R2: 0.11125: 10%|█ | 523/5000 [00:21<03:00, 24.78it/s]
Current R2: 0.11125/Best R2: 0.11125: 11%|█ | 526/5000 [00:21<02:59, 24.94it/s]
Current R2: 0.11125/Best R2: 0.11125: 11%|█ | 526/5000 [00:21<02:59, 24.94it/s]
Current R2: 0.11125/Best R2: 0.11125: 11%|█ | 526/5000 [00:21<02:59, 24.94it/s]
Current R2: 0.11125/Best R2: 0.11125: 11%|█ | 526/5000 [00:21<02:59, 24.94it/s]
Current R2: 0.11125/Best R2: 0.11125: 11%|█ | 529/5000 [00:21<02:57, 25.13it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 529/5000 [00:21<02:57, 25.13it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 529/5000 [00:21<02:57, 25.13it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 529/5000 [00:21<02:57, 25.13it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 532/5000 [00:21<03:00, 24.82it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 532/5000 [00:21<03:00, 24.82it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 532/5000 [00:21<03:00, 24.82it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 532/5000 [00:21<03:00, 24.82it/s]
Current R2: 0.11126/Best R2: 0.11126: 11%|█ | 535/5000 [00:21<03:02, 24.49it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 535/5000 [00:21<03:02, 24.49it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 535/5000 [00:21<03:02, 24.49it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 535/5000 [00:21<03:02, 24.49it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 538/5000 [00:21<03:01, 24.60it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 538/5000 [00:21<03:01, 24.60it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 538/5000 [00:21<03:01, 24.60it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 538/5000 [00:21<03:01, 24.60it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 541/5000 [00:21<03:13, 23.03it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 541/5000 [00:21<03:13, 23.03it/s]
Current R2: 0.11127/Best R2: 0.11127: 11%|█ | 541/5000 [00:21<03:13, 23.03it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 541/5000 [00:22<03:13, 23.03it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 544/5000 [00:22<03:07, 23.81it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 544/5000 [00:22<03:07, 23.81it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 544/5000 [00:22<03:07, 23.81it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 544/5000 [00:22<03:07, 23.81it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 547/5000 [00:22<03:04, 24.12it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 547/5000 [00:22<03:04, 24.12it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 547/5000 [00:22<03:04, 24.12it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 547/5000 [00:22<03:04, 24.12it/s]
Current R2: 0.11128/Best R2: 0.11128: 11%|█ | 550/5000 [00:22<03:11, 23.21it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 550/5000 [00:22<03:11, 23.21it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 550/5000 [00:22<03:11, 23.21it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 550/5000 [00:22<03:11, 23.21it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 553/5000 [00:22<03:05, 23.99it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 553/5000 [00:22<03:05, 23.99it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 553/5000 [00:22<03:05, 23.99it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 553/5000 [00:22<03:05, 23.99it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 556/5000 [00:22<03:01, 24.53it/s]
Current R2: 0.11129/Best R2: 0.11129: 11%|█ | 556/5000 [00:22<03:01, 24.53it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 556/5000 [00:22<03:01, 24.53it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 556/5000 [00:22<03:01, 24.53it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 559/5000 [00:22<02:57, 24.99it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 559/5000 [00:22<02:57, 24.99it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 559/5000 [00:22<02:57, 24.99it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 559/5000 [00:22<02:57, 24.99it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 562/5000 [00:22<02:57, 25.00it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 562/5000 [00:22<02:57, 25.00it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 562/5000 [00:22<02:57, 25.00it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█ | 562/5000 [00:22<02:57, 25.00it/s]
Current R2: 0.11130/Best R2: 0.11130: 11%|█▏ | 565/5000 [00:22<02:56, 25.12it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 565/5000 [00:22<02:56, 25.12it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 565/5000 [00:22<02:56, 25.12it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 565/5000 [00:22<02:56, 25.12it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 568/5000 [00:22<02:54, 25.45it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 568/5000 [00:23<02:54, 25.45it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 568/5000 [00:23<02:54, 25.45it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 568/5000 [00:23<02:54, 25.45it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 571/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 571/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11131/Best R2: 0.11131: 11%|█▏ | 571/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11132/Best R2: 0.11132: 11%|█▏ | 571/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11132/Best R2: 0.11132: 11%|█▏ | 574/5000 [00:23<02:50, 25.98it/s]
Current R2: 0.11132/Best R2: 0.11132: 11%|█▏ | 574/5000 [00:23<02:50, 25.98it/s]
Current R2: 0.11132/Best R2: 0.11132: 11%|█▏ | 574/5000 [00:23<02:50, 25.98it/s]
Current R2: 0.11132/Best R2: 0.11132: 11%|█▏ | 574/5000 [00:23<02:50, 25.98it/s]
Current R2: 0.11132/Best R2: 0.11132: 12%|█▏ | 577/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11132/Best R2: 0.11132: 12%|█▏ | 577/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11132/Best R2: 0.11132: 12%|█▏ | 577/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11132/Best R2: 0.11132: 12%|█▏ | 577/5000 [00:23<02:50, 25.90it/s]
Current R2: 0.11132/Best R2: 0.11132: 12%|█▏ | 580/5000 [00:23<02:50, 25.85it/s]
Current R2: 0.11132/Best R2: 0.11132: 12%|█▏ | 580/5000 [00:23<02:50, 25.85it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 580/5000 [00:23<02:50, 25.85it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 580/5000 [00:23<02:50, 25.85it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 583/5000 [00:23<02:51, 25.69it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 583/5000 [00:23<02:51, 25.69it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 583/5000 [00:23<02:51, 25.69it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 583/5000 [00:23<02:51, 25.69it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 586/5000 [00:23<02:52, 25.65it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 586/5000 [00:23<02:52, 25.65it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 586/5000 [00:23<02:52, 25.65it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 586/5000 [00:23<02:52, 25.65it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 589/5000 [00:23<02:51, 25.74it/s]
Current R2: 0.11133/Best R2: 0.11133: 12%|█▏ | 589/5000 [00:23<02:51, 25.74it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 589/5000 [00:23<02:51, 25.74it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 589/5000 [00:23<02:51, 25.74it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 592/5000 [00:23<02:51, 25.77it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 592/5000 [00:23<02:51, 25.77it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 592/5000 [00:23<02:51, 25.77it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 592/5000 [00:24<02:51, 25.77it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 595/5000 [00:24<02:49, 25.97it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 595/5000 [00:24<02:49, 25.97it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 595/5000 [00:24<02:49, 25.97it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 595/5000 [00:24<02:49, 25.97it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 598/5000 [00:24<02:48, 26.13it/s]
Current R2: 0.11134/Best R2: 0.11134: 12%|█▏ | 598/5000 [00:24<02:48, 26.13it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 598/5000 [00:24<02:48, 26.13it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 598/5000 [00:24<02:48, 26.13it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 601/5000 [00:24<02:47, 26.27it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 601/5000 [00:24<02:47, 26.27it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 601/5000 [00:24<02:47, 26.27it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 601/5000 [00:24<02:47, 26.27it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 604/5000 [00:24<02:47, 26.23it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 604/5000 [00:24<02:47, 26.23it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 604/5000 [00:24<02:47, 26.23it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 604/5000 [00:24<02:47, 26.23it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 607/5000 [00:24<02:47, 26.28it/s]
Current R2: 0.11135/Best R2: 0.11135: 12%|█▏ | 607/5000 [00:24<02:47, 26.28it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 607/5000 [00:24<02:47, 26.28it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 607/5000 [00:24<02:47, 26.28it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 610/5000 [00:24<02:45, 26.56it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 610/5000 [00:24<02:45, 26.56it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 610/5000 [00:24<02:45, 26.56it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 610/5000 [00:24<02:45, 26.56it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 613/5000 [00:24<02:46, 26.34it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 613/5000 [00:24<02:46, 26.34it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 613/5000 [00:24<02:46, 26.34it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 613/5000 [00:24<02:46, 26.34it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 616/5000 [00:24<02:45, 26.48it/s]
Current R2: 0.11136/Best R2: 0.11136: 12%|█▏ | 616/5000 [00:24<02:45, 26.48it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 616/5000 [00:24<02:45, 26.48it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 616/5000 [00:24<02:45, 26.48it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 619/5000 [00:24<02:45, 26.42it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 619/5000 [00:24<02:45, 26.42it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 619/5000 [00:24<02:45, 26.42it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 619/5000 [00:25<02:45, 26.42it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 622/5000 [00:25<02:44, 26.63it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 622/5000 [00:25<02:44, 26.63it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 622/5000 [00:25<02:44, 26.63it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▏ | 622/5000 [00:25<02:44, 26.63it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▎ | 625/5000 [00:25<02:54, 25.13it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▎ | 625/5000 [00:25<02:54, 25.13it/s]
Current R2: 0.11137/Best R2: 0.11137: 12%|█▎ | 625/5000 [00:25<02:54, 25.13it/s]
Current R2: 0.11138/Best R2: 0.11138: 12%|█▎ | 625/5000 [00:25<02:54, 25.13it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 628/5000 [00:25<02:50, 25.60it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 628/5000 [00:25<02:50, 25.60it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 628/5000 [00:25<02:50, 25.60it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 628/5000 [00:25<02:50, 25.60it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 631/5000 [00:25<02:49, 25.72it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 631/5000 [00:25<02:49, 25.72it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 631/5000 [00:25<02:49, 25.72it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 631/5000 [00:25<02:49, 25.72it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 634/5000 [00:25<02:47, 26.10it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 634/5000 [00:25<02:47, 26.10it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 634/5000 [00:25<02:47, 26.10it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 634/5000 [00:25<02:47, 26.10it/s]
Current R2: 0.11138/Best R2: 0.11138: 13%|█▎ | 637/5000 [00:25<02:46, 26.18it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 637/5000 [00:25<02:46, 26.18it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 637/5000 [00:25<02:46, 26.18it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 637/5000 [00:25<02:46, 26.18it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 640/5000 [00:25<02:44, 26.43it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 640/5000 [00:25<02:44, 26.43it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 640/5000 [00:25<02:44, 26.43it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 640/5000 [00:25<02:44, 26.43it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 643/5000 [00:25<02:45, 26.38it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 643/5000 [00:25<02:45, 26.38it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 643/5000 [00:25<02:45, 26.38it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 643/5000 [00:25<02:45, 26.38it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 646/5000 [00:25<02:45, 26.24it/s]
Current R2: 0.11139/Best R2: 0.11139: 13%|█▎ | 646/5000 [00:25<02:45, 26.24it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 646/5000 [00:26<02:45, 26.24it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 646/5000 [00:26<02:45, 26.24it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 649/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 649/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 649/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 649/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 652/5000 [00:26<02:43, 26.53it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 652/5000 [00:26<02:43, 26.53it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 652/5000 [00:26<02:43, 26.53it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 652/5000 [00:26<02:43, 26.53it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 655/5000 [00:26<02:44, 26.42it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 655/5000 [00:26<02:44, 26.42it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 655/5000 [00:26<02:44, 26.42it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 655/5000 [00:26<02:44, 26.42it/s]
Current R2: 0.11140/Best R2: 0.11140: 13%|█▎ | 658/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 658/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 658/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 658/5000 [00:26<02:45, 26.31it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 661/5000 [00:26<02:45, 26.26it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 661/5000 [00:26<02:45, 26.26it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 661/5000 [00:26<02:45, 26.26it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 661/5000 [00:26<02:45, 26.26it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 664/5000 [00:26<02:46, 26.00it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 664/5000 [00:26<02:46, 26.00it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 664/5000 [00:26<02:46, 26.00it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 664/5000 [00:26<02:46, 26.00it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 667/5000 [00:26<02:44, 26.30it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 667/5000 [00:26<02:44, 26.30it/s]
Current R2: 0.11141/Best R2: 0.11141: 13%|█▎ | 667/5000 [00:26<02:44, 26.30it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 667/5000 [00:26<02:44, 26.30it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 670/5000 [00:26<02:43, 26.49it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 670/5000 [00:26<02:43, 26.49it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 670/5000 [00:26<02:43, 26.49it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 670/5000 [00:26<02:43, 26.49it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 673/5000 [00:26<02:42, 26.64it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 673/5000 [00:27<02:42, 26.64it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 673/5000 [00:27<02:42, 26.64it/s]
Current R2: 0.11142/Best R2: 0.11142: 13%|█▎ | 673/5000 [00:27<02:42, 26.64it/s]
Current R2: 0.11142/Best R2: 0.11142: 14%|█▎ | 676/5000 [00:27<02:42, 26.63it/s]
Current R2: 0.11142/Best R2: 0.11142: 14%|█▎ | 676/5000 [00:27<02:42, 26.63it/s]
Current R2: 0.11142/Best R2: 0.11142: 14%|█▎ | 676/5000 [00:27<02:42, 26.63it/s]
Current R2: 0.11142/Best R2: 0.11142: 14%|█▎ | 676/5000 [00:27<02:42, 26.63it/s]
Current R2: 0.11142/Best R2: 0.11142: 14%|█▎ | 679/5000 [00:27<02:41, 26.70it/s]
Current R2: 0.11142/Best R2: 0.11142: 14%|█▎ | 679/5000 [00:27<02:41, 26.70it/s]
Current R2: 0.11142/Best R2: 0.11142: 14%|█▎ | 679/5000 [00:27<02:41, 26.70it/s]
Current R2: 0.11143/Best R2: 0.11143: 14%|█▎ | 679/5000 [00:27<02:41, 26.70it/s]
Current R2: 0.11143/Best R2: 0.11143: 14%|█▎ | 682/5000 [00:27<02:42, 26.58it/s]
Current R2: 0.11143/Best R2: 0.11143: 14%|█▎ | 682/5000 [00:27<02:42, 26.58it/s]
Current R2: 0.11143/Best R2: 0.11143: 14%|█▎ | 682/5000 [00:27<02:53, 24.89it/s]
r2_grid r2_fitted
voxel
0 0.036488 0.051389
1 0.040585 0.058747
2 0.047144 0.080217
3 0.143448 0.148239
4 0.211523 0.212532
5 0.065858 0.065920
6 0.055774 0.056330
7 0.251562 0.253399
8 0.072291 0.076073
x_fit y_fit sd_fit ... sd_true baseline_true amplitude_true
voxel ...
0 7.565219 -4.825470 0.187724 ... 2.5 0.0 1.0
1 -3.532762 -4.159721 0.160052 ... 2.5 0.0 1.0
2 4.408824 -4.892267 0.302635 ... 2.5 0.0 1.0
3 -9.853408 -0.580022 2.445197 ... 2.5 0.0 1.0
4 1.574684 0.427402 3.558650 ... 2.5 0.0 1.0
5 6.287241 -0.351601 2.868563 ... 2.5 0.0 1.0
6 -5.242510 2.810725 1.054553 ... 2.5 0.0 1.0
7 0.142175 2.801231 1.787271 ... 2.5 0.0 1.0
8 8.739098 7.486192 3.787716 ... 2.5 0.0 1.0
[9 rows x 10 columns]
# Decode the *stimulus* from "unseen" data:
# First we need to fit a noise model
from braincoder.optimize import ResidualFitter
resid_fitter = ResidualFitter(model, data, stimulus, parameters=pars)
omega, dof = resid_fitter.fit()
# Simulate new "unseen" data
unseen_data = model.simulate(noise=1.)
# For stimulus reconstruction, we slightly downsample the stimulus space
# otherwise the optimization takes too long on a CPU
# we can do that by simply setting up a new model with a different grid
data = load_szinte2024(resize_factor=2.5)
grid_coordinates = data['grid_coordinates']
stimulus = data['stimulus']
model = GaussianPRF2DWithHRF(grid_coordinates=grid_coordinates,
parameters=parameters,
hrf_model=SPMHRFModel(tr=data['tr']))
# We set up a stimulus fitter
from braincoder.optimize import StimulusFitter
stim_fitter = StimulusFitter(unseen_data, model, omega)
# Legacy Adam is a bit faster than the default Adam optimizer on M1
# Learning rate of 1.0 is a bit high, but works well here
reconstructed_stimulus = stim_fitter.fit(min_n_iterations=200, max_n_iterations=200, learning_rate=.1)
# Here we make a movie of the decoded stimulus
# Set up a function to draw a single frame
vmin, vmax = 0.0, np.quantile(reconstructed_stimulus.values.ravel(), 0.95)
def update(frame):
plt.clf() # Clear the current figure
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
plt.axis('off')
plt.title(f"Frame {frame}")
# Create the animation
fig = plt.figure()
ani = FuncAnimation(fig, update, frames=range(stimulus.shape[0]), interval=100)
HTML(ani.to_html5_video())
init_tau: 0.8406278491020203, 1.023587703704834
USING A PSEUDO-WWT!
WWT max: 0.6844109892845154
0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1819.2310 (best: 1819.2310, rho: 0.100, sigma2: 0.001, mean tau: 0.9222: 0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1818.3887 (best: 1818.3887, rho: 0.098, sigma2: 0.001, mean tau: 0.9343: 0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1817.9341 (best: 1817.9341, rho: 0.096, sigma2: 0.001, mean tau: 0.9422: 0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1817.6691 (best: 1817.6691, rho: 0.095, sigma2: 0.001, mean tau: 0.9464: 0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1817.4301 (best: 1817.4301, rho: 0.093, sigma2: 0.001, mean tau: 0.9473: 0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1817.1289 (best: 1817.1289, rho: 0.091, sigma2: 0.001, mean tau: 0.9461: 0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1816.7709 (best: 1816.7709, rho: 0.090, sigma2: 0.001, mean tau: 0.9437: 0%| | 0/1000 [00:00<?, ?it/s]
fit stat: 1816.7709 (best: 1816.7709, rho: 0.090, sigma2: 0.001, mean tau: 0.9437: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1816.3934 (best: 1816.3934, rho: 0.088, sigma2: 0.001, mean tau: 0.9409: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1816.0304 (best: 1816.0304, rho: 0.087, sigma2: 0.001, mean tau: 0.9379: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1815.7029 (best: 1815.7029, rho: 0.085, sigma2: 0.001, mean tau: 0.9351: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1815.4150 (best: 1815.4150, rho: 0.084, sigma2: 0.001, mean tau: 0.9325: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1815.1556 (best: 1815.1556, rho: 0.082, sigma2: 0.001, mean tau: 0.9303: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1814.9048 (best: 1814.9048, rho: 0.081, sigma2: 0.001, mean tau: 0.9287: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1814.6460 (best: 1814.6460, rho: 0.079, sigma2: 0.001, mean tau: 0.9278: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1814.3757 (best: 1814.3757, rho: 0.078, sigma2: 0.001, mean tau: 0.9276: 1%| | 7/1000 [00:00<00:14, 69.47it/s]
fit stat: 1814.3757 (best: 1814.3757, rho: 0.078, sigma2: 0.001, mean tau: 0.9276: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1814.1011 (best: 1814.1011, rho: 0.077, sigma2: 0.001, mean tau: 0.9279: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1813.8340 (best: 1813.8340, rho: 0.075, sigma2: 0.001, mean tau: 0.9287: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1813.5813 (best: 1813.5813, rho: 0.074, sigma2: 0.001, mean tau: 0.9297: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1813.3456 (best: 1813.3456, rho: 0.073, sigma2: 0.001, mean tau: 0.9306: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1813.1238 (best: 1813.1238, rho: 0.071, sigma2: 0.001, mean tau: 0.9313: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1812.9116 (best: 1812.9116, rho: 0.070, sigma2: 0.001, mean tau: 0.9317: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1812.7046 (best: 1812.7046, rho: 0.069, sigma2: 0.001, mean tau: 0.9315: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1812.4991 (best: 1812.4991, rho: 0.068, sigma2: 0.001, mean tau: 0.9309: 2%|▏ | 15/1000 [00:00<00:13, 74.68it/s]
fit stat: 1812.4991 (best: 1812.4991, rho: 0.068, sigma2: 0.001, mean tau: 0.9309: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1812.2938 (best: 1812.2938, rho: 0.067, sigma2: 0.001, mean tau: 0.9300: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1812.0905 (best: 1812.0905, rho: 0.066, sigma2: 0.001, mean tau: 0.9288: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1811.8933 (best: 1811.8933, rho: 0.064, sigma2: 0.002, mean tau: 0.9274: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1811.7062 (best: 1811.7062, rho: 0.063, sigma2: 0.002, mean tau: 0.9261: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1811.5300 (best: 1811.5300, rho: 0.062, sigma2: 0.002, mean tau: 0.9249: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1811.3622 (best: 1811.3622, rho: 0.061, sigma2: 0.002, mean tau: 0.9240: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1811.1993 (best: 1811.1993, rho: 0.060, sigma2: 0.002, mean tau: 0.9233: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1811.0391 (best: 1811.0391, rho: 0.060, sigma2: 0.002, mean tau: 0.9229: 2%|▏ | 23/1000 [00:00<00:12, 76.40it/s]
fit stat: 1811.0391 (best: 1811.0391, rho: 0.060, sigma2: 0.002, mean tau: 0.9229: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1810.8809 (best: 1810.8809, rho: 0.059, sigma2: 0.002, mean tau: 0.9228: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1810.7257 (best: 1810.7257, rho: 0.058, sigma2: 0.002, mean tau: 0.9229: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1810.5756 (best: 1810.5756, rho: 0.057, sigma2: 0.002, mean tau: 0.9231: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1810.4319 (best: 1810.4319, rho: 0.056, sigma2: 0.002, mean tau: 0.9234: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1810.2952 (best: 1810.2952, rho: 0.055, sigma2: 0.002, mean tau: 0.9236: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1810.1638 (best: 1810.1638, rho: 0.054, sigma2: 0.002, mean tau: 0.9236: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1810.0364 (best: 1810.0364, rho: 0.053, sigma2: 0.002, mean tau: 0.9234: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1809.9114 (best: 1809.9114, rho: 0.053, sigma2: 0.002, mean tau: 0.9231: 3%|▎ | 31/1000 [00:00<00:12, 77.21it/s]
fit stat: 1809.9114 (best: 1809.9114, rho: 0.053, sigma2: 0.002, mean tau: 0.9231: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.7893 (best: 1809.7893, rho: 0.052, sigma2: 0.002, mean tau: 0.9225: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.6709 (best: 1809.6709, rho: 0.051, sigma2: 0.002, mean tau: 0.9219: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.5568 (best: 1809.5568, rho: 0.050, sigma2: 0.002, mean tau: 0.9213: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.4465 (best: 1809.4465, rho: 0.050, sigma2: 0.002, mean tau: 0.9207: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.3406 (best: 1809.3406, rho: 0.049, sigma2: 0.002, mean tau: 0.9202: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.2379 (best: 1809.2379, rho: 0.048, sigma2: 0.002, mean tau: 0.9198: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.1385 (best: 1809.1385, rho: 0.048, sigma2: 0.002, mean tau: 0.9194: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.0419 (best: 1809.0419, rho: 0.047, sigma2: 0.002, mean tau: 0.9191: 4%|▍ | 39/1000 [00:00<00:12, 77.46it/s]
fit stat: 1809.0419 (best: 1809.0419, rho: 0.047, sigma2: 0.002, mean tau: 0.9191: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.9479 (best: 1808.9479, rho: 0.047, sigma2: 0.002, mean tau: 0.9190: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.8563 (best: 1808.8563, rho: 0.046, sigma2: 0.002, mean tau: 0.9189: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.7679 (best: 1808.7679, rho: 0.045, sigma2: 0.002, mean tau: 0.9188: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.6826 (best: 1808.6826, rho: 0.045, sigma2: 0.002, mean tau: 0.9189: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.6000 (best: 1808.6000, rho: 0.044, sigma2: 0.002, mean tau: 0.9189: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.5199 (best: 1808.5199, rho: 0.044, sigma2: 0.002, mean tau: 0.9189: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.4423 (best: 1808.4423, rho: 0.043, sigma2: 0.002, mean tau: 0.9188: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.3667 (best: 1808.3667, rho: 0.043, sigma2: 0.002, mean tau: 0.9185: 5%|▍ | 47/1000 [00:00<00:12, 76.68it/s]
fit stat: 1808.3667 (best: 1808.3667, rho: 0.043, sigma2: 0.002, mean tau: 0.9185: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1808.2932 (best: 1808.2932, rho: 0.042, sigma2: 0.002, mean tau: 0.9181: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1808.2217 (best: 1808.2217, rho: 0.042, sigma2: 0.002, mean tau: 0.9177: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1808.1527 (best: 1808.1527, rho: 0.041, sigma2: 0.002, mean tau: 0.9173: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1808.0856 (best: 1808.0856, rho: 0.041, sigma2: 0.002, mean tau: 0.9169: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1808.0208 (best: 1808.0208, rho: 0.040, sigma2: 0.002, mean tau: 0.9167: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1807.9575 (best: 1807.9575, rho: 0.040, sigma2: 0.002, mean tau: 0.9165: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1807.8961 (best: 1807.8961, rho: 0.039, sigma2: 0.002, mean tau: 0.9164: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1807.8364 (best: 1807.8364, rho: 0.039, sigma2: 0.002, mean tau: 0.9164: 6%|▌ | 55/1000 [00:00<00:12, 77.70it/s]
fit stat: 1807.8364 (best: 1807.8364, rho: 0.039, sigma2: 0.002, mean tau: 0.9164: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.7781 (best: 1807.7781, rho: 0.038, sigma2: 0.002, mean tau: 0.9164: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.7216 (best: 1807.7216, rho: 0.038, sigma2: 0.002, mean tau: 0.9164: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.6666 (best: 1807.6666, rho: 0.038, sigma2: 0.002, mean tau: 0.9164: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.6133 (best: 1807.6133, rho: 0.037, sigma2: 0.002, mean tau: 0.9163: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.5614 (best: 1807.5614, rho: 0.037, sigma2: 0.002, mean tau: 0.9162: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.5106 (best: 1807.5106, rho: 0.036, sigma2: 0.002, mean tau: 0.9160: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.4614 (best: 1807.4614, rho: 0.036, sigma2: 0.002, mean tau: 0.9158: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.4135 (best: 1807.4135, rho: 0.036, sigma2: 0.002, mean tau: 0.9156: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.3666 (best: 1807.3666, rho: 0.035, sigma2: 0.002, mean tau: 0.9154: 6%|▋ | 63/1000 [00:00<00:12, 77.73it/s]
fit stat: 1807.3666 (best: 1807.3666, rho: 0.035, sigma2: 0.002, mean tau: 0.9154: 7%|▋ | 72/1000 [00:00<00:11, 78.44it/s]
fit stat: 1807.3210 (best: 1807.3210, rho: 0.035, sigma2: 0.002, mean tau: 0.9153: 7%|▋ | 72/1000 [00:00<00:11, 78.44it/s]
fit stat: 1807.2769 (best: 1807.2769, rho: 0.035, sigma2: 0.002, mean tau: 0.9152: 7%|▋ | 72/1000 [00:00<00:11, 78.44it/s]
fit stat: 1807.2335 (best: 1807.2335, rho: 0.034, sigma2: 0.002, mean tau: 0.9151: 7%|▋ | 72/1000 [00:00<00:11, 78.44it/s]
fit stat: 1807.1913 (best: 1807.1913, rho: 0.034, sigma2: 0.002, mean tau: 0.9152: 7%|▋ | 72/1000 [00:00<00:11, 78.44it/s]
fit stat: 1807.1500 (best: 1807.1500, rho: 0.034, sigma2: 0.002, mean tau: 0.9152: 7%|▋ | 72/1000 [00:00<00:11, 78.44it/s]
fit stat: 1807.1100 (best: 1807.1100, rho: 0.033, sigma2: 0.002, mean tau: 0.9152: 7%|▋ | 72/1000 [00:01<00:11, 78.44it/s]
fit stat: 1807.0708 (best: 1807.0708, rho: 0.033, sigma2: 0.002, mean tau: 0.9152: 7%|▋ | 72/1000 [00:01<00:11, 78.44it/s]
fit stat: 1807.0326 (best: 1807.0326, rho: 0.033, sigma2: 0.002, mean tau: 0.9151: 7%|▋ | 72/1000 [00:01<00:11, 78.44it/s]
fit stat: 1807.0326 (best: 1807.0326, rho: 0.033, sigma2: 0.002, mean tau: 0.9151: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.9954 (best: 1806.9954, rho: 0.032, sigma2: 0.002, mean tau: 0.9150: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.9589 (best: 1806.9589, rho: 0.032, sigma2: 0.002, mean tau: 0.9150: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.9232 (best: 1806.9232, rho: 0.032, sigma2: 0.002, mean tau: 0.9149: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.8884 (best: 1806.8884, rho: 0.032, sigma2: 0.002, mean tau: 0.9149: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.8544 (best: 1806.8544, rho: 0.031, sigma2: 0.002, mean tau: 0.9149: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.8212 (best: 1806.8212, rho: 0.031, sigma2: 0.002, mean tau: 0.9148: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.7888 (best: 1806.7888, rho: 0.031, sigma2: 0.002, mean tau: 0.9148: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.7570 (best: 1806.7570, rho: 0.030, sigma2: 0.002, mean tau: 0.9148: 8%|▊ | 80/1000 [00:01<00:11, 78.01it/s]
fit stat: 1806.7570 (best: 1806.7570, rho: 0.030, sigma2: 0.002, mean tau: 0.9148: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.7258 (best: 1806.7258, rho: 0.030, sigma2: 0.002, mean tau: 0.9148: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.6954 (best: 1806.6954, rho: 0.030, sigma2: 0.002, mean tau: 0.9148: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.6658 (best: 1806.6658, rho: 0.030, sigma2: 0.002, mean tau: 0.9148: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.6366 (best: 1806.6366, rho: 0.029, sigma2: 0.002, mean tau: 0.9149: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.6082 (best: 1806.6082, rho: 0.029, sigma2: 0.002, mean tau: 0.9149: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.5802 (best: 1806.5802, rho: 0.029, sigma2: 0.002, mean tau: 0.9149: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.5529 (best: 1806.5529, rho: 0.029, sigma2: 0.002, mean tau: 0.9150: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.5260 (best: 1806.5260, rho: 0.028, sigma2: 0.002, mean tau: 0.9150: 9%|▉ | 88/1000 [00:01<00:11, 78.36it/s]
fit stat: 1806.5260 (best: 1806.5260, rho: 0.028, sigma2: 0.002, mean tau: 0.9150: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.4998 (best: 1806.4998, rho: 0.028, sigma2: 0.002, mean tau: 0.9150: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.4741 (best: 1806.4741, rho: 0.028, sigma2: 0.002, mean tau: 0.9150: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.4489 (best: 1806.4489, rho: 0.028, sigma2: 0.002, mean tau: 0.9150: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.4241 (best: 1806.4241, rho: 0.028, sigma2: 0.002, mean tau: 0.9151: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.3999 (best: 1806.3999, rho: 0.027, sigma2: 0.002, mean tau: 0.9151: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.3761 (best: 1806.3761, rho: 0.027, sigma2: 0.002, mean tau: 0.9151: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.3529 (best: 1806.3529, rho: 0.027, sigma2: 0.002, mean tau: 0.9152: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.3301 (best: 1806.3301, rho: 0.027, sigma2: 0.002, mean tau: 0.9153: 10%|▉ | 96/1000 [00:01<00:11, 78.34it/s]
fit stat: 1806.3301 (best: 1806.3301, rho: 0.027, sigma2: 0.002, mean tau: 0.9153: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.3075 (best: 1806.3075, rho: 0.027, sigma2: 0.002, mean tau: 0.9153: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.2855 (best: 1806.2855, rho: 0.026, sigma2: 0.002, mean tau: 0.9154: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.2639 (best: 1806.2639, rho: 0.026, sigma2: 0.002, mean tau: 0.9154: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.2427 (best: 1806.2427, rho: 0.026, sigma2: 0.002, mean tau: 0.9155: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.2220 (best: 1806.2220, rho: 0.026, sigma2: 0.002, mean tau: 0.9155: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.2015 (best: 1806.2015, rho: 0.026, sigma2: 0.002, mean tau: 0.9156: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.1815 (best: 1806.1815, rho: 0.025, sigma2: 0.002, mean tau: 0.9156: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.1617 (best: 1806.1617, rho: 0.025, sigma2: 0.002, mean tau: 0.9157: 10%|█ | 104/1000 [00:01<00:11, 78.19it/s]
fit stat: 1806.1617 (best: 1806.1617, rho: 0.025, sigma2: 0.002, mean tau: 0.9157: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.1423 (best: 1806.1423, rho: 0.025, sigma2: 0.002, mean tau: 0.9157: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.1233 (best: 1806.1233, rho: 0.025, sigma2: 0.002, mean tau: 0.9158: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.1046 (best: 1806.1046, rho: 0.025, sigma2: 0.002, mean tau: 0.9159: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.0864 (best: 1806.0864, rho: 0.025, sigma2: 0.002, mean tau: 0.9159: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.0682 (best: 1806.0682, rho: 0.024, sigma2: 0.002, mean tau: 0.9160: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.0507 (best: 1806.0507, rho: 0.024, sigma2: 0.002, mean tau: 0.9161: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.0332 (best: 1806.0332, rho: 0.024, sigma2: 0.002, mean tau: 0.9161: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1806.0161 (best: 1806.0161, rho: 0.024, sigma2: 0.002, mean tau: 0.9162: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1805.9993 (best: 1805.9993, rho: 0.024, sigma2: 0.002, mean tau: 0.9162: 11%|█ | 112/1000 [00:01<00:11, 78.47it/s]
fit stat: 1805.9993 (best: 1805.9993, rho: 0.024, sigma2: 0.002, mean tau: 0.9162: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.9827 (best: 1805.9827, rho: 0.024, sigma2: 0.002, mean tau: 0.9163: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.9664 (best: 1805.9664, rho: 0.023, sigma2: 0.002, mean tau: 0.9163: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.9506 (best: 1805.9506, rho: 0.023, sigma2: 0.002, mean tau: 0.9164: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.9346 (best: 1805.9346, rho: 0.023, sigma2: 0.002, mean tau: 0.9165: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.9191 (best: 1805.9191, rho: 0.023, sigma2: 0.002, mean tau: 0.9165: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.9038 (best: 1805.9038, rho: 0.023, sigma2: 0.002, mean tau: 0.9166: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.8889 (best: 1805.8889, rho: 0.023, sigma2: 0.002, mean tau: 0.9167: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.8741 (best: 1805.8741, rho: 0.023, sigma2: 0.002, mean tau: 0.9167: 12%|█▏ | 121/1000 [00:01<00:11, 79.25it/s]
fit stat: 1805.8741 (best: 1805.8741, rho: 0.023, sigma2: 0.002, mean tau: 0.9167: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.8596 (best: 1805.8596, rho: 0.022, sigma2: 0.002, mean tau: 0.9168: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.8453 (best: 1805.8453, rho: 0.022, sigma2: 0.002, mean tau: 0.9168: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.8313 (best: 1805.8313, rho: 0.022, sigma2: 0.002, mean tau: 0.9169: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.8175 (best: 1805.8175, rho: 0.022, sigma2: 0.002, mean tau: 0.9170: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.8040 (best: 1805.8040, rho: 0.022, sigma2: 0.002, mean tau: 0.9170: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.7905 (best: 1805.7905, rho: 0.022, sigma2: 0.002, mean tau: 0.9171: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.7772 (best: 1805.7772, rho: 0.022, sigma2: 0.002, mean tau: 0.9171: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.7643 (best: 1805.7643, rho: 0.021, sigma2: 0.002, mean tau: 0.9172: 13%|█▎ | 129/1000 [00:01<00:11, 78.95it/s]
fit stat: 1805.7643 (best: 1805.7643, rho: 0.021, sigma2: 0.002, mean tau: 0.9172: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.7515 (best: 1805.7515, rho: 0.021, sigma2: 0.001, mean tau: 0.9173: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.7389 (best: 1805.7389, rho: 0.021, sigma2: 0.001, mean tau: 0.9173: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.7263 (best: 1805.7263, rho: 0.021, sigma2: 0.001, mean tau: 0.9174: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.7142 (best: 1805.7142, rho: 0.021, sigma2: 0.001, mean tau: 0.9174: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.7021 (best: 1805.7021, rho: 0.021, sigma2: 0.001, mean tau: 0.9175: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.6904 (best: 1805.6904, rho: 0.021, sigma2: 0.001, mean tau: 0.9175: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.6786 (best: 1805.6786, rho: 0.021, sigma2: 0.001, mean tau: 0.9176: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.6672 (best: 1805.6672, rho: 0.020, sigma2: 0.001, mean tau: 0.9176: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.6558 (best: 1805.6558, rho: 0.020, sigma2: 0.001, mean tau: 0.9177: 14%|█▎ | 137/1000 [00:01<00:10, 78.83it/s]
fit stat: 1805.6558 (best: 1805.6558, rho: 0.020, sigma2: 0.001, mean tau: 0.9177: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.6447 (best: 1805.6447, rho: 0.020, sigma2: 0.001, mean tau: 0.9177: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.6335 (best: 1805.6335, rho: 0.020, sigma2: 0.001, mean tau: 0.9178: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.6228 (best: 1805.6228, rho: 0.020, sigma2: 0.001, mean tau: 0.9178: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.6119 (best: 1805.6119, rho: 0.020, sigma2: 0.001, mean tau: 0.9179: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.6014 (best: 1805.6014, rho: 0.020, sigma2: 0.001, mean tau: 0.9179: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.5911 (best: 1805.5911, rho: 0.020, sigma2: 0.001, mean tau: 0.9180: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.5807 (best: 1805.5807, rho: 0.020, sigma2: 0.001, mean tau: 0.9180: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.5707 (best: 1805.5707, rho: 0.020, sigma2: 0.001, mean tau: 0.9181: 15%|█▍ | 146/1000 [00:01<00:10, 79.83it/s]
fit stat: 1805.5707 (best: 1805.5707, rho: 0.020, sigma2: 0.001, mean tau: 0.9181: 15%|█▌ | 154/1000 [00:01<00:10, 79.74it/s]
fit stat: 1805.5608 (best: 1805.5608, rho: 0.019, sigma2: 0.001, mean tau: 0.9181: 15%|█▌ | 154/1000 [00:01<00:10, 79.74it/s]
fit stat: 1805.5510 (best: 1805.5510, rho: 0.019, sigma2: 0.001, mean tau: 0.9182: 15%|█▌ | 154/1000 [00:01<00:10, 79.74it/s]
fit stat: 1805.5411 (best: 1805.5411, rho: 0.019, sigma2: 0.001, mean tau: 0.9182: 15%|█▌ | 154/1000 [00:02<00:10, 79.74it/s]
fit stat: 1805.5316 (best: 1805.5316, rho: 0.019, sigma2: 0.001, mean tau: 0.9183: 15%|█▌ | 154/1000 [00:02<00:10, 79.74it/s]
fit stat: 1805.5222 (best: 1805.5222, rho: 0.019, sigma2: 0.001, mean tau: 0.9183: 15%|█▌ | 154/1000 [00:02<00:10, 79.74it/s]
fit stat: 1805.5129 (best: 1805.5129, rho: 0.019, sigma2: 0.001, mean tau: 0.9184: 15%|█▌ | 154/1000 [00:02<00:10, 79.74it/s]
fit stat: 1805.5038 (best: 1805.5038, rho: 0.019, sigma2: 0.001, mean tau: 0.9184: 15%|█▌ | 154/1000 [00:02<00:10, 79.74it/s]
fit stat: 1805.4948 (best: 1805.4948, rho: 0.019, sigma2: 0.001, mean tau: 0.9184: 15%|█▌ | 154/1000 [00:02<00:10, 79.74it/s]
fit stat: 1805.4858 (best: 1805.4858, rho: 0.019, sigma2: 0.001, mean tau: 0.9185: 15%|█▌ | 154/1000 [00:02<00:10, 79.74it/s]
fit stat: 1805.4858 (best: 1805.4858, rho: 0.019, sigma2: 0.001, mean tau: 0.9185: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4771 (best: 1805.4771, rho: 0.019, sigma2: 0.001, mean tau: 0.9185: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4684 (best: 1805.4684, rho: 0.018, sigma2: 0.001, mean tau: 0.9186: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4598 (best: 1805.4598, rho: 0.018, sigma2: 0.001, mean tau: 0.9186: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4514 (best: 1805.4514, rho: 0.018, sigma2: 0.001, mean tau: 0.9186: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4431 (best: 1805.4431, rho: 0.018, sigma2: 0.001, mean tau: 0.9187: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4348 (best: 1805.4348, rho: 0.018, sigma2: 0.001, mean tau: 0.9187: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4268 (best: 1805.4268, rho: 0.018, sigma2: 0.001, mean tau: 0.9188: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4187 (best: 1805.4187, rho: 0.018, sigma2: 0.001, mean tau: 0.9188: 16%|█▋ | 163/1000 [00:02<00:10, 79.95it/s]
fit stat: 1805.4187 (best: 1805.4187, rho: 0.018, sigma2: 0.001, mean tau: 0.9188: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.4108 (best: 1805.4108, rho: 0.018, sigma2: 0.001, mean tau: 0.9188: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.4030 (best: 1805.4030, rho: 0.018, sigma2: 0.001, mean tau: 0.9189: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.3954 (best: 1805.3954, rho: 0.018, sigma2: 0.001, mean tau: 0.9189: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.3877 (best: 1805.3877, rho: 0.018, sigma2: 0.001, mean tau: 0.9189: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.3801 (best: 1805.3801, rho: 0.017, sigma2: 0.001, mean tau: 0.9190: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.3727 (best: 1805.3727, rho: 0.017, sigma2: 0.001, mean tau: 0.9190: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.3654 (best: 1805.3654, rho: 0.017, sigma2: 0.001, mean tau: 0.9190: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.3582 (best: 1805.3582, rho: 0.017, sigma2: 0.001, mean tau: 0.9191: 17%|█▋ | 171/1000 [00:02<00:10, 79.14it/s]
fit stat: 1805.3582 (best: 1805.3582, rho: 0.017, sigma2: 0.001, mean tau: 0.9191: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3510 (best: 1805.3510, rho: 0.017, sigma2: 0.001, mean tau: 0.9191: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3440 (best: 1805.3440, rho: 0.017, sigma2: 0.001, mean tau: 0.9191: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3370 (best: 1805.3370, rho: 0.017, sigma2: 0.001, mean tau: 0.9192: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3301 (best: 1805.3301, rho: 0.017, sigma2: 0.001, mean tau: 0.9192: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3232 (best: 1805.3232, rho: 0.017, sigma2: 0.001, mean tau: 0.9192: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3167 (best: 1805.3167, rho: 0.017, sigma2: 0.001, mean tau: 0.9193: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3099 (best: 1805.3099, rho: 0.017, sigma2: 0.001, mean tau: 0.9193: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3035 (best: 1805.3035, rho: 0.017, sigma2: 0.001, mean tau: 0.9193: 18%|█▊ | 179/1000 [00:02<00:10, 78.54it/s]
fit stat: 1805.3035 (best: 1805.3035, rho: 0.017, sigma2: 0.001, mean tau: 0.9193: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2969 (best: 1805.2969, rho: 0.016, sigma2: 0.001, mean tau: 0.9193: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2906 (best: 1805.2906, rho: 0.016, sigma2: 0.001, mean tau: 0.9194: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2842 (best: 1805.2842, rho: 0.016, sigma2: 0.001, mean tau: 0.9194: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2781 (best: 1805.2781, rho: 0.016, sigma2: 0.001, mean tau: 0.9194: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2719 (best: 1805.2719, rho: 0.016, sigma2: 0.001, mean tau: 0.9195: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2657 (best: 1805.2657, rho: 0.016, sigma2: 0.001, mean tau: 0.9195: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2596 (best: 1805.2596, rho: 0.016, sigma2: 0.001, mean tau: 0.9195: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2538 (best: 1805.2538, rho: 0.016, sigma2: 0.001, mean tau: 0.9195: 19%|█▊ | 187/1000 [00:02<00:10, 78.17it/s]
fit stat: 1805.2538 (best: 1805.2538, rho: 0.016, sigma2: 0.001, mean tau: 0.9195: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2479 (best: 1805.2479, rho: 0.016, sigma2: 0.001, mean tau: 0.9196: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2421 (best: 1805.2421, rho: 0.016, sigma2: 0.001, mean tau: 0.9196: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2363 (best: 1805.2363, rho: 0.016, sigma2: 0.001, mean tau: 0.9196: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2307 (best: 1805.2307, rho: 0.016, sigma2: 0.001, mean tau: 0.9196: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2251 (best: 1805.2251, rho: 0.016, sigma2: 0.001, mean tau: 0.9197: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2195 (best: 1805.2195, rho: 0.016, sigma2: 0.001, mean tau: 0.9197: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2141 (best: 1805.2141, rho: 0.016, sigma2: 0.001, mean tau: 0.9197: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2086 (best: 1805.2086, rho: 0.015, sigma2: 0.001, mean tau: 0.9197: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2031 (best: 1805.2031, rho: 0.015, sigma2: 0.001, mean tau: 0.9197: 20%|█▉ | 195/1000 [00:02<00:10, 77.74it/s]
fit stat: 1805.2031 (best: 1805.2031, rho: 0.015, sigma2: 0.001, mean tau: 0.9197: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1978 (best: 1805.1978, rho: 0.015, sigma2: 0.001, mean tau: 0.9198: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1926 (best: 1805.1926, rho: 0.015, sigma2: 0.001, mean tau: 0.9198: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1875 (best: 1805.1875, rho: 0.015, sigma2: 0.001, mean tau: 0.9198: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1824 (best: 1805.1824, rho: 0.015, sigma2: 0.001, mean tau: 0.9198: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1774 (best: 1805.1774, rho: 0.015, sigma2: 0.001, mean tau: 0.9199: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1721 (best: 1805.1721, rho: 0.015, sigma2: 0.001, mean tau: 0.9199: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1671 (best: 1805.1671, rho: 0.015, sigma2: 0.001, mean tau: 0.9199: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1625 (best: 1805.1625, rho: 0.015, sigma2: 0.001, mean tau: 0.9199: 20%|██ | 204/1000 [00:02<00:10, 78.35it/s]
fit stat: 1805.1625 (best: 1805.1625, rho: 0.015, sigma2: 0.001, mean tau: 0.9199: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1576 (best: 1805.1576, rho: 0.015, sigma2: 0.001, mean tau: 0.9199: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1527 (best: 1805.1527, rho: 0.015, sigma2: 0.001, mean tau: 0.9200: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1479 (best: 1805.1479, rho: 0.015, sigma2: 0.001, mean tau: 0.9200: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1433 (best: 1805.1433, rho: 0.015, sigma2: 0.001, mean tau: 0.9200: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1387 (best: 1805.1387, rho: 0.015, sigma2: 0.001, mean tau: 0.9200: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1340 (best: 1805.1340, rho: 0.015, sigma2: 0.001, mean tau: 0.9200: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1294 (best: 1805.1294, rho: 0.014, sigma2: 0.001, mean tau: 0.9200: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1250 (best: 1805.1250, rho: 0.014, sigma2: 0.001, mean tau: 0.9201: 21%|██ | 212/1000 [00:02<00:10, 78.33it/s]
fit stat: 1805.1250 (best: 1805.1250, rho: 0.014, sigma2: 0.001, mean tau: 0.9201: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.1205 (best: 1805.1205, rho: 0.014, sigma2: 0.001, mean tau: 0.9201: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.1162 (best: 1805.1162, rho: 0.014, sigma2: 0.001, mean tau: 0.9201: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.1118 (best: 1805.1118, rho: 0.014, sigma2: 0.001, mean tau: 0.9201: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.1073 (best: 1805.1073, rho: 0.014, sigma2: 0.001, mean tau: 0.9201: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.1031 (best: 1805.1031, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.0989 (best: 1805.0989, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.0947 (best: 1805.0947, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.0907 (best: 1805.0907, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 22%|██▏ | 220/1000 [00:02<00:09, 78.05it/s]
fit stat: 1805.0907 (best: 1805.0907, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 23%|██▎ | 228/1000 [00:02<00:09, 77.86it/s]
fit stat: 1805.0865 (best: 1805.0865, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 23%|██▎ | 228/1000 [00:02<00:09, 77.86it/s]
fit stat: 1805.0824 (best: 1805.0824, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 23%|██▎ | 228/1000 [00:02<00:09, 77.86it/s]
fit stat: 1805.0782 (best: 1805.0782, rho: 0.014, sigma2: 0.001, mean tau: 0.9202: 23%|██▎ | 228/1000 [00:02<00:09, 77.86it/s]
fit stat: 1805.0746 (best: 1805.0746, rho: 0.014, sigma2: 0.001, mean tau: 0.9203: 23%|██▎ | 228/1000 [00:02<00:09, 77.86it/s]
fit stat: 1805.0706 (best: 1805.0706, rho: 0.014, sigma2: 0.001, mean tau: 0.9203: 23%|██▎ | 228/1000 [00:02<00:09, 77.86it/s]
fit stat: 1805.0665 (best: 1805.0665, rho: 0.014, sigma2: 0.001, mean tau: 0.9203: 23%|██▎ | 228/1000 [00:02<00:09, 77.86it/s]
fit stat: 1805.0626 (best: 1805.0626, rho: 0.014, sigma2: 0.001, mean tau: 0.9203: 23%|██▎ | 228/1000 [00:03<00:09, 77.86it/s]
fit stat: 1805.0587 (best: 1805.0587, rho: 0.014, sigma2: 0.001, mean tau: 0.9203: 23%|██▎ | 228/1000 [00:03<00:09, 77.86it/s]
fit stat: 1805.0587 (best: 1805.0587, rho: 0.014, sigma2: 0.001, mean tau: 0.9203: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0552 (best: 1805.0552, rho: 0.014, sigma2: 0.001, mean tau: 0.9203: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0515 (best: 1805.0515, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0479 (best: 1805.0479, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0439 (best: 1805.0439, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0403 (best: 1805.0403, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0367 (best: 1805.0367, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0333 (best: 1805.0333, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0297 (best: 1805.0297, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▎ | 236/1000 [00:03<00:09, 78.08it/s]
fit stat: 1805.0297 (best: 1805.0297, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0261 (best: 1805.0261, rho: 0.013, sigma2: 0.001, mean tau: 0.9204: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0227 (best: 1805.0227, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0192 (best: 1805.0192, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0159 (best: 1805.0159, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0125 (best: 1805.0125, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0092 (best: 1805.0092, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0060 (best: 1805.0060, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0026 (best: 1805.0026, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 24%|██▍ | 244/1000 [00:03<00:09, 77.90it/s]
fit stat: 1805.0026 (best: 1805.0026, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9993 (best: 1804.9993, rho: 0.013, sigma2: 0.001, mean tau: 0.9205: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9961 (best: 1804.9961, rho: 0.013, sigma2: 0.001, mean tau: 0.9206: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9928 (best: 1804.9928, rho: 0.013, sigma2: 0.001, mean tau: 0.9206: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9897 (best: 1804.9897, rho: 0.013, sigma2: 0.001, mean tau: 0.9206: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9867 (best: 1804.9867, rho: 0.013, sigma2: 0.001, mean tau: 0.9206: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9834 (best: 1804.9834, rho: 0.013, sigma2: 0.001, mean tau: 0.9206: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9805 (best: 1804.9805, rho: 0.013, sigma2: 0.001, mean tau: 0.9206: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9774 (best: 1804.9774, rho: 0.012, sigma2: 0.001, mean tau: 0.9206: 25%|██▌ | 252/1000 [00:03<00:09, 76.58it/s]
fit stat: 1804.9774 (best: 1804.9774, rho: 0.012, sigma2: 0.001, mean tau: 0.9206: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9744 (best: 1804.9744, rho: 0.012, sigma2: 0.000, mean tau: 0.9206: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9713 (best: 1804.9713, rho: 0.012, sigma2: 0.000, mean tau: 0.9206: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9685 (best: 1804.9685, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9656 (best: 1804.9656, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9625 (best: 1804.9625, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9598 (best: 1804.9598, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9569 (best: 1804.9569, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9541 (best: 1804.9541, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 26%|██▌ | 260/1000 [00:03<00:09, 77.26it/s]
fit stat: 1804.9541 (best: 1804.9541, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9513 (best: 1804.9513, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9485 (best: 1804.9485, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9458 (best: 1804.9458, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9430 (best: 1804.9430, rho: 0.012, sigma2: 0.000, mean tau: 0.9207: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9402 (best: 1804.9402, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9374 (best: 1804.9374, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9349 (best: 1804.9349, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9324 (best: 1804.9324, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9297 (best: 1804.9297, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 27%|██▋ | 268/1000 [00:03<00:09, 77.76it/s]
fit stat: 1804.9297 (best: 1804.9297, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9270 (best: 1804.9270, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9244 (best: 1804.9244, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9220 (best: 1804.9220, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9194 (best: 1804.9194, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9169 (best: 1804.9169, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9143 (best: 1804.9143, rho: 0.012, sigma2: 0.000, mean tau: 0.9208: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9120 (best: 1804.9120, rho: 0.012, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9095 (best: 1804.9095, rho: 0.012, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 277/1000 [00:03<00:09, 78.68it/s]
fit stat: 1804.9095 (best: 1804.9095, rho: 0.012, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.9070 (best: 1804.9070, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.9047 (best: 1804.9047, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.9023 (best: 1804.9023, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.8998 (best: 1804.8998, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.8976 (best: 1804.8976, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.8951 (best: 1804.8951, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.8929 (best: 1804.8929, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.8906 (best: 1804.8906, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 28%|██▊ | 285/1000 [00:03<00:09, 77.71it/s]
fit stat: 1804.8906 (best: 1804.8906, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8882 (best: 1804.8882, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8860 (best: 1804.8860, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8837 (best: 1804.8837, rho: 0.011, sigma2: 0.000, mean tau: 0.9209: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8816 (best: 1804.8816, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8794 (best: 1804.8794, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8771 (best: 1804.8771, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8751 (best: 1804.8751, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8730 (best: 1804.8730, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 29%|██▉ | 293/1000 [00:03<00:09, 75.97it/s]
fit stat: 1804.8730 (best: 1804.8730, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8707 (best: 1804.8707, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8687 (best: 1804.8687, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8665 (best: 1804.8665, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8644 (best: 1804.8644, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8623 (best: 1804.8623, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8604 (best: 1804.8604, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8584 (best: 1804.8584, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8562 (best: 1804.8562, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 30%|███ | 301/1000 [00:03<00:09, 75.38it/s]
fit stat: 1804.8562 (best: 1804.8562, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 31%|███ | 309/1000 [00:03<00:09, 76.63it/s]
fit stat: 1804.8542 (best: 1804.8542, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 31%|███ | 309/1000 [00:03<00:09, 76.63it/s]
fit stat: 1804.8523 (best: 1804.8523, rho: 0.011, sigma2: 0.000, mean tau: 0.9210: 31%|███ | 309/1000 [00:03<00:09, 76.63it/s]
fit stat: 1804.8502 (best: 1804.8502, rho: 0.011, sigma2: 0.000, mean tau: 0.9211: 31%|███ | 309/1000 [00:04<00:09, 76.63it/s]
fit stat: 1804.8484 (best: 1804.8484, rho: 0.011, sigma2: 0.000, mean tau: 0.9211: 31%|███ | 309/1000 [00:04<00:09, 76.63it/s]
fit stat: 1804.8466 (best: 1804.8466, rho: 0.011, sigma2: 0.000, mean tau: 0.9211: 31%|███ | 309/1000 [00:04<00:09, 76.63it/s]
fit stat: 1804.8445 (best: 1804.8445, rho: 0.011, sigma2: 0.000, mean tau: 0.9211: 31%|███ | 309/1000 [00:04<00:09, 76.63it/s]
fit stat: 1804.8427 (best: 1804.8427, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 31%|███ | 309/1000 [00:04<00:09, 76.63it/s]
fit stat: 1804.8407 (best: 1804.8407, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 31%|███ | 309/1000 [00:04<00:09, 76.63it/s]
fit stat: 1804.8407 (best: 1804.8407, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8387 (best: 1804.8387, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8369 (best: 1804.8369, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8351 (best: 1804.8351, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8331 (best: 1804.8331, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8313 (best: 1804.8313, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8296 (best: 1804.8296, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8278 (best: 1804.8278, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8258 (best: 1804.8258, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▏ | 317/1000 [00:04<00:08, 77.11it/s]
fit stat: 1804.8258 (best: 1804.8258, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8243 (best: 1804.8243, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8224 (best: 1804.8224, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8208 (best: 1804.8208, rho: 0.010, sigma2: 0.000, mean tau: 0.9211: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8188 (best: 1804.8188, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8173 (best: 1804.8173, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8156 (best: 1804.8156, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8136 (best: 1804.8136, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8121 (best: 1804.8121, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8104 (best: 1804.8104, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 32%|███▎ | 325/1000 [00:04<00:08, 77.30it/s]
fit stat: 1804.8104 (best: 1804.8104, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.8087 (best: 1804.8087, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.8071 (best: 1804.8071, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.8055 (best: 1804.8055, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.8037 (best: 1804.8037, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.8021 (best: 1804.8021, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.8007 (best: 1804.8007, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.7990 (best: 1804.7990, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.7975 (best: 1804.7975, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 33%|███▎ | 334/1000 [00:04<00:08, 78.29it/s]
fit stat: 1804.7975 (best: 1804.7975, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7959 (best: 1804.7959, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7942 (best: 1804.7942, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7928 (best: 1804.7928, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7913 (best: 1804.7913, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7897 (best: 1804.7897, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7880 (best: 1804.7880, rho: 0.010, sigma2: 0.000, mean tau: 0.9212: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7866 (best: 1804.7866, rho: 0.010, sigma2: 0.000, mean tau: 0.9213: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7852 (best: 1804.7852, rho: 0.010, sigma2: 0.000, mean tau: 0.9213: 34%|███▍ | 342/1000 [00:04<00:08, 78.68it/s]
fit stat: 1804.7852 (best: 1804.7852, rho: 0.010, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7836 (best: 1804.7836, rho: 0.010, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7821 (best: 1804.7821, rho: 0.010, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7808 (best: 1804.7808, rho: 0.010, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7791 (best: 1804.7791, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7777 (best: 1804.7777, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7764 (best: 1804.7764, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7748 (best: 1804.7748, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7733 (best: 1804.7733, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 35%|███▌ | 350/1000 [00:04<00:08, 79.06it/s]
fit stat: 1804.7733 (best: 1804.7733, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7720 (best: 1804.7720, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7706 (best: 1804.7706, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7693 (best: 1804.7693, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7678 (best: 1804.7678, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7665 (best: 1804.7665, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7651 (best: 1804.7651, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7639 (best: 1804.7639, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7626 (best: 1804.7626, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 36%|███▌ | 358/1000 [00:04<00:08, 78.14it/s]
fit stat: 1804.7626 (best: 1804.7626, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7610 (best: 1804.7610, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7598 (best: 1804.7598, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7583 (best: 1804.7583, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7570 (best: 1804.7570, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7559 (best: 1804.7559, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7548 (best: 1804.7548, rho: 0.009, sigma2: 0.000, mean tau: 0.9213: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7532 (best: 1804.7532, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7520 (best: 1804.7520, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 366/1000 [00:04<00:08, 77.96it/s]
fit stat: 1804.7520 (best: 1804.7520, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7506 (best: 1804.7506, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7494 (best: 1804.7494, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7482 (best: 1804.7482, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7468 (best: 1804.7468, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7456 (best: 1804.7456, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7444 (best: 1804.7444, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7432 (best: 1804.7432, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7419 (best: 1804.7419, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 37%|███▋ | 374/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7419 (best: 1804.7419, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7407 (best: 1804.7407, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7396 (best: 1804.7396, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7384 (best: 1804.7384, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7371 (best: 1804.7371, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7361 (best: 1804.7361, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7349 (best: 1804.7349, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:04<00:08, 77.04it/s]
fit stat: 1804.7338 (best: 1804.7338, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:05<00:08, 77.04it/s]
fit stat: 1804.7325 (best: 1804.7325, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 38%|███▊ | 382/1000 [00:05<00:08, 77.04it/s]
fit stat: 1804.7325 (best: 1804.7325, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7314 (best: 1804.7314, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7302 (best: 1804.7302, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7291 (best: 1804.7291, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7279 (best: 1804.7279, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7268 (best: 1804.7268, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7256 (best: 1804.7256, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7246 (best: 1804.7246, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7234 (best: 1804.7234, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 39%|███▉ | 390/1000 [00:05<00:08, 75.73it/s]
fit stat: 1804.7234 (best: 1804.7234, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7225 (best: 1804.7225, rho: 0.009, sigma2: 0.000, mean tau: 0.9214: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7214 (best: 1804.7214, rho: 0.008, sigma2: 0.000, mean tau: 0.9214: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7202 (best: 1804.7202, rho: 0.008, sigma2: 0.000, mean tau: 0.9214: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7191 (best: 1804.7191, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7180 (best: 1804.7180, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7170 (best: 1804.7170, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7159 (best: 1804.7159, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7150 (best: 1804.7150, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 40%|███▉ | 398/1000 [00:05<00:08, 73.25it/s]
fit stat: 1804.7150 (best: 1804.7150, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7139 (best: 1804.7139, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7128 (best: 1804.7128, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7118 (best: 1804.7118, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7108 (best: 1804.7108, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7097 (best: 1804.7097, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7087 (best: 1804.7087, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7076 (best: 1804.7076, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7067 (best: 1804.7067, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7056 (best: 1804.7056, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 41%|████ | 406/1000 [00:05<00:08, 73.93it/s]
fit stat: 1804.7056 (best: 1804.7056, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.7048 (best: 1804.7048, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.7037 (best: 1804.7037, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.7028 (best: 1804.7028, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.7018 (best: 1804.7018, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.7008 (best: 1804.7008, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.6998 (best: 1804.6998, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.6990 (best: 1804.6990, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.6980 (best: 1804.6980, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.6969 (best: 1804.6969, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 415/1000 [00:05<00:07, 75.81it/s]
fit stat: 1804.6969 (best: 1804.6969, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6959 (best: 1804.6959, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6951 (best: 1804.6951, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6941 (best: 1804.6941, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6931 (best: 1804.6931, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6923 (best: 1804.6923, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6914 (best: 1804.6914, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6904 (best: 1804.6904, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6896 (best: 1804.6896, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 42%|████▏ | 424/1000 [00:05<00:07, 77.01it/s]
fit stat: 1804.6896 (best: 1804.6896, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6887 (best: 1804.6887, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6877 (best: 1804.6877, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6869 (best: 1804.6869, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6859 (best: 1804.6859, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6852 (best: 1804.6852, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6842 (best: 1804.6842, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6833 (best: 1804.6833, rho: 0.008, sigma2: 0.000, mean tau: 0.9215: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6825 (best: 1804.6825, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 43%|████▎ | 432/1000 [00:05<00:07, 77.80it/s]
fit stat: 1804.6825 (best: 1804.6825, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6818 (best: 1804.6818, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6807 (best: 1804.6807, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6799 (best: 1804.6799, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6790 (best: 1804.6790, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6782 (best: 1804.6782, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6772 (best: 1804.6772, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6765 (best: 1804.6765, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6757 (best: 1804.6757, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 44%|████▍ | 440/1000 [00:05<00:07, 77.91it/s]
fit stat: 1804.6757 (best: 1804.6757, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6748 (best: 1804.6748, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6740 (best: 1804.6740, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6732 (best: 1804.6732, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6725 (best: 1804.6725, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6715 (best: 1804.6715, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6707 (best: 1804.6707, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6700 (best: 1804.6700, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6693 (best: 1804.6693, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 45%|████▍ | 448/1000 [00:05<00:07, 78.28it/s]
fit stat: 1804.6693 (best: 1804.6693, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6683 (best: 1804.6683, rho: 0.008, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6676 (best: 1804.6676, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6667 (best: 1804.6667, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6660 (best: 1804.6660, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6653 (best: 1804.6653, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6646 (best: 1804.6646, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6638 (best: 1804.6638, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6630 (best: 1804.6630, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▌ | 456/1000 [00:05<00:06, 78.57it/s]
fit stat: 1804.6630 (best: 1804.6630, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:05<00:06, 78.42it/s]
fit stat: 1804.6622 (best: 1804.6622, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:05<00:06, 78.42it/s]
fit stat: 1804.6614 (best: 1804.6614, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:05<00:06, 78.42it/s]
fit stat: 1804.6606 (best: 1804.6606, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:06<00:06, 78.42it/s]
fit stat: 1804.6600 (best: 1804.6600, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:06<00:06, 78.42it/s]
fit stat: 1804.6592 (best: 1804.6592, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:06<00:06, 78.42it/s]
fit stat: 1804.6584 (best: 1804.6584, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:06<00:06, 78.42it/s]
fit stat: 1804.6577 (best: 1804.6577, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:06<00:06, 78.42it/s]
fit stat: 1804.6570 (best: 1804.6570, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 46%|████▋ | 464/1000 [00:06<00:06, 78.42it/s]
fit stat: 1804.6570 (best: 1804.6570, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6562 (best: 1804.6562, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6556 (best: 1804.6556, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6548 (best: 1804.6548, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6542 (best: 1804.6542, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6533 (best: 1804.6533, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6528 (best: 1804.6528, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6520 (best: 1804.6520, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6512 (best: 1804.6512, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 47%|████▋ | 472/1000 [00:06<00:06, 78.79it/s]
fit stat: 1804.6512 (best: 1804.6512, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6505 (best: 1804.6505, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6498 (best: 1804.6498, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6490 (best: 1804.6490, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6484 (best: 1804.6484, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6477 (best: 1804.6477, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6472 (best: 1804.6472, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6464 (best: 1804.6464, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6456 (best: 1804.6456, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 48%|████▊ | 480/1000 [00:06<00:06, 78.87it/s]
fit stat: 1804.6456 (best: 1804.6456, rho: 0.007, sigma2: 0.000, mean tau: 0.9216: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6450 (best: 1804.6450, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6443 (best: 1804.6443, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6437 (best: 1804.6437, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6431 (best: 1804.6431, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6425 (best: 1804.6425, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6416 (best: 1804.6416, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6411 (best: 1804.6411, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6405 (best: 1804.6405, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 49%|████▉ | 488/1000 [00:06<00:06, 78.82it/s]
fit stat: 1804.6405 (best: 1804.6405, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6398 (best: 1804.6398, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6390 (best: 1804.6390, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6384 (best: 1804.6384, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6379 (best: 1804.6379, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6372 (best: 1804.6372, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6366 (best: 1804.6366, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6360 (best: 1804.6360, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6353 (best: 1804.6353, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6346 (best: 1804.6346, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|████▉ | 496/1000 [00:06<00:06, 78.62it/s]
fit stat: 1804.6346 (best: 1804.6346, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6342 (best: 1804.6342, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6335 (best: 1804.6335, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6328 (best: 1804.6328, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6322 (best: 1804.6322, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6316 (best: 1804.6316, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6311 (best: 1804.6311, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6304 (best: 1804.6304, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6298 (best: 1804.6298, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6293 (best: 1804.6293, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 50%|█████ | 505/1000 [00:06<00:06, 78.99it/s]
fit stat: 1804.6293 (best: 1804.6293, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6285 (best: 1804.6285, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6282 (best: 1804.6282, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6274 (best: 1804.6274, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6268 (best: 1804.6268, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6262 (best: 1804.6262, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6256 (best: 1804.6256, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6250 (best: 1804.6250, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6245 (best: 1804.6245, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 51%|█████▏ | 514/1000 [00:06<00:06, 79.77it/s]
fit stat: 1804.6245 (best: 1804.6245, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6240 (best: 1804.6240, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6234 (best: 1804.6234, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6227 (best: 1804.6227, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6223 (best: 1804.6223, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6216 (best: 1804.6216, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6211 (best: 1804.6211, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6205 (best: 1804.6205, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6200 (best: 1804.6200, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 52%|█████▏ | 522/1000 [00:06<00:05, 79.74it/s]
fit stat: 1804.6200 (best: 1804.6200, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6194 (best: 1804.6194, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6189 (best: 1804.6189, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6184 (best: 1804.6184, rho: 0.007, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6177 (best: 1804.6177, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6173 (best: 1804.6173, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6167 (best: 1804.6167, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6161 (best: 1804.6161, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6156 (best: 1804.6156, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 53%|█████▎ | 530/1000 [00:06<00:05, 79.48it/s]
fit stat: 1804.6156 (best: 1804.6156, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6151 (best: 1804.6151, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6146 (best: 1804.6146, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6140 (best: 1804.6140, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6135 (best: 1804.6135, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6130 (best: 1804.6130, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6124 (best: 1804.6124, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6119 (best: 1804.6119, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:06<00:05, 78.83it/s]
fit stat: 1804.6115 (best: 1804.6115, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:07<00:05, 78.83it/s]
fit stat: 1804.6111 (best: 1804.6111, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 54%|█████▍ | 538/1000 [00:07<00:05, 78.83it/s]
fit stat: 1804.6111 (best: 1804.6111, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6105 (best: 1804.6105, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6099 (best: 1804.6099, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6094 (best: 1804.6094, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6089 (best: 1804.6089, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6084 (best: 1804.6084, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6079 (best: 1804.6079, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6072 (best: 1804.6072, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6068 (best: 1804.6068, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 55%|█████▍ | 547/1000 [00:07<00:05, 79.10it/s]
fit stat: 1804.6068 (best: 1804.6068, rho: 0.006, sigma2: 0.000, mean tau: 0.9217: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6063 (best: 1804.6063, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6060 (best: 1804.6060, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6052 (best: 1804.6052, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6050 (best: 1804.6050, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6044 (best: 1804.6044, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6040 (best: 1804.6040, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6034 (best: 1804.6034, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6030 (best: 1804.6030, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▌ | 555/1000 [00:07<00:05, 79.14it/s]
fit stat: 1804.6030 (best: 1804.6030, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.6024 (best: 1804.6024, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.6019 (best: 1804.6019, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.6016 (best: 1804.6016, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.6010 (best: 1804.6010, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.6005 (best: 1804.6005, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.6001 (best: 1804.6001, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.5996 (best: 1804.5996, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.5991 (best: 1804.5991, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 56%|█████▋ | 563/1000 [00:07<00:05, 79.08it/s]
fit stat: 1804.5991 (best: 1804.5991, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5985 (best: 1804.5985, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5981 (best: 1804.5981, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5978 (best: 1804.5978, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5973 (best: 1804.5973, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5969 (best: 1804.5969, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5963 (best: 1804.5963, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5961 (best: 1804.5961, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5955 (best: 1804.5955, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 57%|█████▋ | 571/1000 [00:07<00:05, 79.02it/s]
fit stat: 1804.5955 (best: 1804.5955, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5950 (best: 1804.5950, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5946 (best: 1804.5946, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5942 (best: 1804.5942, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5938 (best: 1804.5938, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5933 (best: 1804.5933, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5928 (best: 1804.5928, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5923 (best: 1804.5923, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5919 (best: 1804.5919, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 58%|█████▊ | 579/1000 [00:07<00:05, 79.09it/s]
fit stat: 1804.5919 (best: 1804.5919, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5916 (best: 1804.5916, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5912 (best: 1804.5912, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5907 (best: 1804.5907, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5905 (best: 1804.5905, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5898 (best: 1804.5898, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5894 (best: 1804.5894, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5890 (best: 1804.5890, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5885 (best: 1804.5885, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 59%|█████▊ | 587/1000 [00:07<00:05, 78.90it/s]
fit stat: 1804.5885 (best: 1804.5885, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5880 (best: 1804.5880, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5878 (best: 1804.5878, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5874 (best: 1804.5874, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5868 (best: 1804.5868, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5864 (best: 1804.5864, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5862 (best: 1804.5862, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5856 (best: 1804.5856, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5852 (best: 1804.5852, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|█████▉ | 595/1000 [00:07<00:05, 79.07it/s]
fit stat: 1804.5852 (best: 1804.5852, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5848 (best: 1804.5848, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5844 (best: 1804.5844, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5840 (best: 1804.5840, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5835 (best: 1804.5835, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5831 (best: 1804.5831, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5828 (best: 1804.5828, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5824 (best: 1804.5824, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5822 (best: 1804.5822, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 60%|██████ | 603/1000 [00:07<00:05, 79.03it/s]
fit stat: 1804.5822 (best: 1804.5822, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5815 (best: 1804.5815, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5813 (best: 1804.5813, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5808 (best: 1804.5808, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5804 (best: 1804.5804, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5801 (best: 1804.5801, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5796 (best: 1804.5796, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5792 (best: 1804.5792, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5790 (best: 1804.5790, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 61%|██████ | 611/1000 [00:07<00:05, 72.97it/s]
fit stat: 1804.5790 (best: 1804.5790, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:07<00:05, 74.55it/s]
fit stat: 1804.5784 (best: 1804.5784, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:07<00:05, 74.55it/s]
fit stat: 1804.5781 (best: 1804.5781, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:07<00:05, 74.55it/s]
fit stat: 1804.5776 (best: 1804.5776, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:07<00:05, 74.55it/s]
fit stat: 1804.5773 (best: 1804.5773, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:08<00:05, 74.55it/s]
fit stat: 1804.5770 (best: 1804.5770, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:08<00:05, 74.55it/s]
fit stat: 1804.5767 (best: 1804.5767, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:08<00:05, 74.55it/s]
fit stat: 1804.5763 (best: 1804.5763, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:08<00:05, 74.55it/s]
fit stat: 1804.5758 (best: 1804.5758, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 62%|██████▏ | 619/1000 [00:08<00:05, 74.55it/s]
fit stat: 1804.5758 (best: 1804.5758, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5754 (best: 1804.5754, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5751 (best: 1804.5751, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5748 (best: 1804.5748, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5743 (best: 1804.5743, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5740 (best: 1804.5740, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5736 (best: 1804.5736, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5734 (best: 1804.5734, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5730 (best: 1804.5730, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 63%|██████▎ | 627/1000 [00:08<00:04, 75.88it/s]
fit stat: 1804.5730 (best: 1804.5730, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5724 (best: 1804.5724, rho: 0.006, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5721 (best: 1804.5721, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5718 (best: 1804.5718, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5717 (best: 1804.5717, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5712 (best: 1804.5712, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5708 (best: 1804.5708, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5704 (best: 1804.5704, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5699 (best: 1804.5699, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▎ | 635/1000 [00:08<00:04, 76.39it/s]
fit stat: 1804.5699 (best: 1804.5699, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5697 (best: 1804.5697, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5693 (best: 1804.5693, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5691 (best: 1804.5691, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5686 (best: 1804.5686, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5684 (best: 1804.5684, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5679 (best: 1804.5679, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5676 (best: 1804.5676, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5674 (best: 1804.5674, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 64%|██████▍ | 643/1000 [00:08<00:04, 76.31it/s]
fit stat: 1804.5674 (best: 1804.5674, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5670 (best: 1804.5670, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5667 (best: 1804.5667, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5664 (best: 1804.5664, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5660 (best: 1804.5660, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5657 (best: 1804.5657, rho: 0.005, sigma2: 0.000, mean tau: 0.9218: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5653 (best: 1804.5653, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5649 (best: 1804.5649, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5646 (best: 1804.5646, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5643 (best: 1804.5643, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 65%|██████▌ | 651/1000 [00:08<00:04, 76.91it/s]
fit stat: 1804.5643 (best: 1804.5643, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5642 (best: 1804.5642, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5637 (best: 1804.5637, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5634 (best: 1804.5634, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5629 (best: 1804.5629, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5626 (best: 1804.5626, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5624 (best: 1804.5624, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5620 (best: 1804.5620, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5616 (best: 1804.5616, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5615 (best: 1804.5615, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 66%|██████▌ | 660/1000 [00:08<00:04, 78.11it/s]
fit stat: 1804.5615 (best: 1804.5615, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5609 (best: 1804.5609, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5608 (best: 1804.5608, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5605 (best: 1804.5605, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5602 (best: 1804.5602, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5598 (best: 1804.5598, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5594 (best: 1804.5594, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5592 (best: 1804.5592, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5590 (best: 1804.5590, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 67%|██████▋ | 669/1000 [00:08<00:04, 78.96it/s]
fit stat: 1804.5590 (best: 1804.5590, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5586 (best: 1804.5586, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5582 (best: 1804.5582, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5580 (best: 1804.5580, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5575 (best: 1804.5575, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5573 (best: 1804.5573, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5570 (best: 1804.5570, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5566 (best: 1804.5566, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5564 (best: 1804.5564, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 677/1000 [00:08<00:04, 78.80it/s]
fit stat: 1804.5564 (best: 1804.5564, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5562 (best: 1804.5562, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5558 (best: 1804.5558, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5555 (best: 1804.5555, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5551 (best: 1804.5551, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5548 (best: 1804.5548, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5546 (best: 1804.5546, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5542 (best: 1804.5542, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5541 (best: 1804.5541, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 68%|██████▊ | 685/1000 [00:08<00:03, 78.85it/s]
fit stat: 1804.5541 (best: 1804.5541, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5537 (best: 1804.5537, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5535 (best: 1804.5535, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5533 (best: 1804.5533, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5530 (best: 1804.5530, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5526 (best: 1804.5526, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5524 (best: 1804.5524, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5521 (best: 1804.5521, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:08<00:03, 78.73it/s]
fit stat: 1804.5518 (best: 1804.5518, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 69%|██████▉ | 693/1000 [00:09<00:03, 78.73it/s]
fit stat: 1804.5518 (best: 1804.5518, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5514 (best: 1804.5514, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5513 (best: 1804.5513, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5510 (best: 1804.5510, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5505 (best: 1804.5505, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5503 (best: 1804.5503, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5499 (best: 1804.5499, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5497 (best: 1804.5497, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5494 (best: 1804.5494, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 70%|███████ | 701/1000 [00:09<00:03, 78.44it/s]
fit stat: 1804.5494 (best: 1804.5494, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5491 (best: 1804.5491, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5490 (best: 1804.5490, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5486 (best: 1804.5486, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5483 (best: 1804.5483, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5481 (best: 1804.5481, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5477 (best: 1804.5477, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5476 (best: 1804.5476, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5472 (best: 1804.5472, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5470 (best: 1804.5470, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 71%|███████ | 709/1000 [00:09<00:03, 77.57it/s]
fit stat: 1804.5470 (best: 1804.5470, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5468 (best: 1804.5468, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5466 (best: 1804.5466, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5461 (best: 1804.5461, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5460 (best: 1804.5460, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5457 (best: 1804.5457, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5454 (best: 1804.5454, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5453 (best: 1804.5453, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5449 (best: 1804.5449, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 72%|███████▏ | 718/1000 [00:09<00:03, 78.21it/s]
fit stat: 1804.5449 (best: 1804.5449, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5448 (best: 1804.5448, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5444 (best: 1804.5444, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5442 (best: 1804.5442, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5438 (best: 1804.5438, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5435 (best: 1804.5435, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5433 (best: 1804.5433, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5431 (best: 1804.5431, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5428 (best: 1804.5428, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 726/1000 [00:09<00:03, 78.58it/s]
fit stat: 1804.5428 (best: 1804.5428, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5426 (best: 1804.5426, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5424 (best: 1804.5424, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5420 (best: 1804.5420, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5417 (best: 1804.5417, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5415 (best: 1804.5415, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5414 (best: 1804.5414, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5410 (best: 1804.5410, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5408 (best: 1804.5408, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 73%|███████▎ | 734/1000 [00:09<00:03, 78.62it/s]
fit stat: 1804.5408 (best: 1804.5408, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5404 (best: 1804.5404, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5403 (best: 1804.5403, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5399 (best: 1804.5399, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5398 (best: 1804.5398, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5397 (best: 1804.5397, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5394 (best: 1804.5394, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5391 (best: 1804.5391, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5388 (best: 1804.5388, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5386 (best: 1804.5386, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 74%|███████▍ | 742/1000 [00:09<00:03, 78.48it/s]
fit stat: 1804.5386 (best: 1804.5386, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5382 (best: 1804.5382, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5381 (best: 1804.5381, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5378 (best: 1804.5378, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5376 (best: 1804.5376, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5372 (best: 1804.5372, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5372 (best: 1804.5372, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5369 (best: 1804.5369, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5366 (best: 1804.5366, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 75%|███████▌ | 751/1000 [00:09<00:03, 79.16it/s]
fit stat: 1804.5366 (best: 1804.5366, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5363 (best: 1804.5363, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5363 (best: 1804.5363, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5359 (best: 1804.5359, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5358 (best: 1804.5358, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5355 (best: 1804.5355, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5352 (best: 1804.5352, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5350 (best: 1804.5350, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5348 (best: 1804.5348, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5345 (best: 1804.5345, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 76%|███████▌ | 759/1000 [00:09<00:03, 79.19it/s]
fit stat: 1804.5345 (best: 1804.5345, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5342 (best: 1804.5342, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5342 (best: 1804.5342, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5339 (best: 1804.5339, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5338 (best: 1804.5338, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5333 (best: 1804.5333, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5332 (best: 1804.5332, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5330 (best: 1804.5330, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5327 (best: 1804.5327, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 77%|███████▋ | 768/1000 [00:09<00:02, 79.34it/s]
fit stat: 1804.5327 (best: 1804.5327, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:09<00:02, 79.18it/s]
fit stat: 1804.5325 (best: 1804.5325, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:09<00:02, 79.18it/s]
fit stat: 1804.5323 (best: 1804.5323, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:09<00:02, 79.18it/s]
fit stat: 1804.5320 (best: 1804.5320, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:09<00:02, 79.18it/s]
fit stat: 1804.5317 (best: 1804.5317, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:10<00:02, 79.18it/s]
fit stat: 1804.5316 (best: 1804.5316, rho: 0.005, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:10<00:02, 79.18it/s]
fit stat: 1804.5314 (best: 1804.5314, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:10<00:02, 79.18it/s]
fit stat: 1804.5311 (best: 1804.5311, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:10<00:02, 79.18it/s]
fit stat: 1804.5310 (best: 1804.5310, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 776/1000 [00:10<00:02, 79.18it/s]
fit stat: 1804.5310 (best: 1804.5310, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5306 (best: 1804.5306, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5305 (best: 1804.5305, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5303 (best: 1804.5303, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5302 (best: 1804.5302, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5299 (best: 1804.5299, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5294 (best: 1804.5294, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5295 (best: 1804.5294, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5292 (best: 1804.5292, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 78%|███████▊ | 784/1000 [00:10<00:02, 79.29it/s]
fit stat: 1804.5292 (best: 1804.5292, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5291 (best: 1804.5291, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5289 (best: 1804.5289, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5287 (best: 1804.5287, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5283 (best: 1804.5283, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5282 (best: 1804.5282, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5278 (best: 1804.5278, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5277 (best: 1804.5277, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5275 (best: 1804.5275, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5273 (best: 1804.5273, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 79%|███████▉ | 792/1000 [00:10<00:02, 79.09it/s]
fit stat: 1804.5273 (best: 1804.5273, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5271 (best: 1804.5271, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5270 (best: 1804.5270, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5267 (best: 1804.5267, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5265 (best: 1804.5265, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5264 (best: 1804.5264, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5261 (best: 1804.5261, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5259 (best: 1804.5259, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5258 (best: 1804.5258, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 80%|████████ | 801/1000 [00:10<00:02, 79.53it/s]
fit stat: 1804.5258 (best: 1804.5258, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5255 (best: 1804.5255, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5254 (best: 1804.5254, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5250 (best: 1804.5250, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5249 (best: 1804.5249, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5247 (best: 1804.5247, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5243 (best: 1804.5243, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5243 (best: 1804.5243, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5242 (best: 1804.5242, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 81%|████████ | 809/1000 [00:10<00:02, 77.67it/s]
fit stat: 1804.5242 (best: 1804.5242, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5239 (best: 1804.5239, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5237 (best: 1804.5237, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5234 (best: 1804.5234, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5234 (best: 1804.5234, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5231 (best: 1804.5231, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5228 (best: 1804.5228, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5226 (best: 1804.5226, rho: 0.004, sigma2: 0.000, mean tau: 0.9219: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5225 (best: 1804.5225, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▏ | 817/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5225 (best: 1804.5225, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5225 (best: 1804.5225, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5221 (best: 1804.5221, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5220 (best: 1804.5220, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5217 (best: 1804.5217, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5215 (best: 1804.5215, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5214 (best: 1804.5214, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5211 (best: 1804.5211, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5210 (best: 1804.5210, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5206 (best: 1804.5206, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 82%|████████▎ | 825/1000 [00:10<00:02, 78.29it/s]
fit stat: 1804.5206 (best: 1804.5206, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5205 (best: 1804.5205, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5204 (best: 1804.5204, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5203 (best: 1804.5203, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5200 (best: 1804.5200, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5199 (best: 1804.5199, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5198 (best: 1804.5198, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5195 (best: 1804.5195, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5193 (best: 1804.5193, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5190 (best: 1804.5190, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 83%|████████▎ | 834/1000 [00:10<00:02, 79.04it/s]
fit stat: 1804.5190 (best: 1804.5190, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5190 (best: 1804.5190, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5187 (best: 1804.5187, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5186 (best: 1804.5186, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5183 (best: 1804.5183, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5183 (best: 1804.5183, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5179 (best: 1804.5179, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5178 (best: 1804.5178, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5176 (best: 1804.5176, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5173 (best: 1804.5173, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 84%|████████▍ | 843/1000 [00:10<00:01, 79.39it/s]
fit stat: 1804.5173 (best: 1804.5173, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:10<00:01, 79.66it/s]
fit stat: 1804.5172 (best: 1804.5172, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:10<00:01, 79.66it/s]
fit stat: 1804.5171 (best: 1804.5171, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:10<00:01, 79.66it/s]
fit stat: 1804.5170 (best: 1804.5170, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:10<00:01, 79.66it/s]
fit stat: 1804.5166 (best: 1804.5166, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:10<00:01, 79.66it/s]
fit stat: 1804.5166 (best: 1804.5166, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:10<00:01, 79.66it/s]
fit stat: 1804.5164 (best: 1804.5164, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:10<00:01, 79.66it/s]
fit stat: 1804.5162 (best: 1804.5162, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:11<00:01, 79.66it/s]
fit stat: 1804.5161 (best: 1804.5161, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:11<00:01, 79.66it/s]
fit stat: 1804.5159 (best: 1804.5159, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 85%|████████▌ | 852/1000 [00:11<00:01, 79.66it/s]
fit stat: 1804.5159 (best: 1804.5159, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5157 (best: 1804.5157, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5154 (best: 1804.5154, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5154 (best: 1804.5154, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5151 (best: 1804.5151, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5149 (best: 1804.5149, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5148 (best: 1804.5148, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5146 (best: 1804.5146, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5144 (best: 1804.5144, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5143 (best: 1804.5143, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 86%|████████▌ | 861/1000 [00:11<00:01, 79.90it/s]
fit stat: 1804.5143 (best: 1804.5143, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5142 (best: 1804.5142, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5139 (best: 1804.5139, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5138 (best: 1804.5138, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5137 (best: 1804.5137, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5134 (best: 1804.5134, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5133 (best: 1804.5133, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5132 (best: 1804.5132, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5128 (best: 1804.5128, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5128 (best: 1804.5128, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 87%|████████▋ | 870/1000 [00:11<00:01, 80.27it/s]
fit stat: 1804.5128 (best: 1804.5128, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5127 (best: 1804.5127, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5125 (best: 1804.5125, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5123 (best: 1804.5123, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5121 (best: 1804.5121, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5118 (best: 1804.5118, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5117 (best: 1804.5117, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5117 (best: 1804.5117, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5115 (best: 1804.5115, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5114 (best: 1804.5114, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 88%|████████▊ | 879/1000 [00:11<00:01, 79.95it/s]
fit stat: 1804.5114 (best: 1804.5114, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5111 (best: 1804.5111, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5110 (best: 1804.5110, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5106 (best: 1804.5106, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5106 (best: 1804.5106, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5104 (best: 1804.5104, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5104 (best: 1804.5104, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5103 (best: 1804.5103, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5100 (best: 1804.5100, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5099 (best: 1804.5099, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 89%|████████▉ | 888/1000 [00:11<00:01, 80.08it/s]
fit stat: 1804.5099 (best: 1804.5099, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5098 (best: 1804.5098, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5095 (best: 1804.5095, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5093 (best: 1804.5093, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5093 (best: 1804.5093, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5090 (best: 1804.5090, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5089 (best: 1804.5089, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5088 (best: 1804.5088, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5087 (best: 1804.5087, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5085 (best: 1804.5085, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 90%|████████▉ | 897/1000 [00:11<00:01, 80.45it/s]
fit stat: 1804.5085 (best: 1804.5085, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5082 (best: 1804.5082, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5081 (best: 1804.5081, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5079 (best: 1804.5079, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5077 (best: 1804.5077, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5076 (best: 1804.5076, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5076 (best: 1804.5076, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5074 (best: 1804.5074, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5072 (best: 1804.5072, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5071 (best: 1804.5071, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 91%|█████████ | 906/1000 [00:11<00:01, 80.11it/s]
fit stat: 1804.5071 (best: 1804.5071, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5068 (best: 1804.5068, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5067 (best: 1804.5067, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5066 (best: 1804.5066, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5065 (best: 1804.5065, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5063 (best: 1804.5063, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5061 (best: 1804.5061, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5060 (best: 1804.5060, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5059 (best: 1804.5059, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 915/1000 [00:11<00:01, 79.67it/s]
fit stat: 1804.5059 (best: 1804.5059, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5056 (best: 1804.5056, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5055 (best: 1804.5055, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5052 (best: 1804.5052, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5051 (best: 1804.5051, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5052 (best: 1804.5051, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5049 (best: 1804.5049, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5048 (best: 1804.5048, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5045 (best: 1804.5045, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 92%|█████████▏| 923/1000 [00:11<00:00, 79.65it/s]
fit stat: 1804.5045 (best: 1804.5045, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5045 (best: 1804.5045, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5044 (best: 1804.5044, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5042 (best: 1804.5042, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5040 (best: 1804.5040, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5038 (best: 1804.5038, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5037 (best: 1804.5037, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5037 (best: 1804.5037, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:11<00:00, 79.27it/s]
fit stat: 1804.5033 (best: 1804.5033, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:12<00:00, 79.27it/s]
fit stat: 1804.5033 (best: 1804.5033, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 93%|█████████▎| 931/1000 [00:12<00:00, 79.27it/s]
fit stat: 1804.5033 (best: 1804.5033, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5032 (best: 1804.5032, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5031 (best: 1804.5031, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5029 (best: 1804.5029, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5028 (best: 1804.5028, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5027 (best: 1804.5027, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5023 (best: 1804.5023, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5023 (best: 1804.5023, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5023 (best: 1804.5023, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5021 (best: 1804.5021, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 94%|█████████▍| 940/1000 [00:12<00:00, 80.48it/s]
fit stat: 1804.5021 (best: 1804.5021, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5020 (best: 1804.5020, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5017 (best: 1804.5017, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5018 (best: 1804.5017, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5015 (best: 1804.5015, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5013 (best: 1804.5013, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5012 (best: 1804.5012, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5011 (best: 1804.5011, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5010 (best: 1804.5010, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5007 (best: 1804.5007, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 95%|█████████▍| 949/1000 [00:12<00:00, 80.81it/s]
fit stat: 1804.5007 (best: 1804.5007, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.5007 (best: 1804.5007, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.5005 (best: 1804.5005, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.5004 (best: 1804.5004, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.5002 (best: 1804.5002, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.5001 (best: 1804.5001, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.5000 (best: 1804.5000, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.4998 (best: 1804.4998, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.4998 (best: 1804.4998, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.4998 (best: 1804.4998, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 96%|█████████▌| 958/1000 [00:12<00:00, 80.36it/s]
fit stat: 1804.4998 (best: 1804.4998, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4995 (best: 1804.4995, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4993 (best: 1804.4993, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4991 (best: 1804.4991, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4991 (best: 1804.4991, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4990 (best: 1804.4990, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4989 (best: 1804.4989, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4987 (best: 1804.4987, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4985 (best: 1804.4985, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4984 (best: 1804.4984, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 97%|█████████▋| 967/1000 [00:12<00:00, 80.13it/s]
fit stat: 1804.4984 (best: 1804.4984, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4982 (best: 1804.4982, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4983 (best: 1804.4982, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4979 (best: 1804.4979, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4979 (best: 1804.4979, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4977 (best: 1804.4977, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4976 (best: 1804.4976, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4974 (best: 1804.4974, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4974 (best: 1804.4974, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 976/1000 [00:12<00:00, 76.63it/s]
fit stat: 1804.4974 (best: 1804.4974, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4973 (best: 1804.4973, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4971 (best: 1804.4971, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4969 (best: 1804.4969, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4968 (best: 1804.4968, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4968 (best: 1804.4968, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4966 (best: 1804.4966, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4965 (best: 1804.4965, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4962 (best: 1804.4962, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 98%|█████████▊| 984/1000 [00:12<00:00, 74.60it/s]
fit stat: 1804.4962 (best: 1804.4962, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4962 (best: 1804.4962, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4961 (best: 1804.4961, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4960 (best: 1804.4960, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4958 (best: 1804.4958, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4956 (best: 1804.4956, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4956 (best: 1804.4956, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4955 (best: 1804.4955, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4954 (best: 1804.4954, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 99%|█████████▉| 992/1000 [00:12<00:00, 75.91it/s]
fit stat: 1804.4954 (best: 1804.4954, rho: 0.004, sigma2: 0.000, mean tau: 0.9220: 100%|██████████| 1000/1000 [00:12<00:00, 78.19it/s]
0%| | 0/200 [00:00<?, ?it/s]
0%| | 0/200 [00:00<?, ?it/s]
0%| | 0/200 [00:00<?, ?it/s]
LL: -2093.3818: 0%| | 0/200 [00:00<?, ?it/s]
LL: -2093.3818: 0%| | 1/200 [00:00<01:54, 1.74it/s]
LL: -2093.3818: 0%| | 1/200 [00:00<01:54, 1.74it/s]
LL: -2093.3818: 1%| | 2/200 [00:00<01:05, 3.04it/s]
LL: -2093.3818: 1%| | 2/200 [00:00<01:05, 3.04it/s]
LL: -2093.3818: 2%|▏ | 3/200 [00:00<00:50, 3.89it/s]
LL: -2093.3818: 2%|▏ | 3/200 [00:01<00:50, 3.89it/s]
LL: -2093.3818: 2%|▏ | 4/200 [00:01<00:42, 4.65it/s]
LL: -2093.3818: 2%|▏ | 4/200 [00:01<00:42, 4.65it/s]
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/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
/private/tmp/claude-1763273667/-Users-gdehol-git-retinonumeral/3d3d2794-a70f-4e6c-9121-c4c2af92cc53/scratchpad/bc_docs/examples/00_encodingdecoding/decode_visual.py:157: FutureWarning: The previous implementation of stack is deprecated and will be removed in a future version of pandas. See the What's New notes for pandas 2.1.0 for details. Specify future_stack=True to adopt the new implementation and silence this warning.
plt.imshow(reconstructed_stimulus.stack('y').loc[frame], cmap='viridis', vmin=vmin, vmax=vmax)
Total running time of the script: (1 minutes 23.225 seconds)