### Regression test of muVT ensemble using flat files
### System: GenericMuVT
## Reading first result
## Reading second result
## Validating ensemble
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# WARNING: Support for muVT ensemble is an experimental feature under current #
#          development. You can help us to improve it by reporting errors     #
#          at https://github.com/shirtsgroup/physical_validation              #
#          Thank you!                                                         #
###############################################################################
Analytical slope of ln(P_2(N)/P_1(N)): 0.20045393
Trajectory 1 equilibration: First 27 frames (2.7% of trajectory) discarded for burn-in.
Trajectory 1 decorrelation: 40 frames (4.1% of equilibrated trajectory) discarded for decorrelation.
Trajectory 1 tails (cut = 0.10%): 2 frames (0.21% of equilibrated and decorrelated trajectory) were cut
After equilibration, decorrelation and tail pruning, 93.11% (932 frames) of original Trajectory 1 remain.
Trajectory 2 equilibration: First 0 frames (0.0% of trajectory) discarded for burn-in.
Trajectory 2 decorrelation: 38 frames (3.8% of equilibrated trajectory) discarded for decorrelation.
Trajectory 2 tails (cut = 0.10%): 1 frames (0.10% of equilibrated and decorrelated trajectory) were cut
After equilibration, decorrelation and tail pruning, 96.10% (962 frames) of original Trajectory 2 remain.
Overlap is 94.2% of trajectory 1 and 77.2% of trajectory 2.
A rule of thumb states that a good overlap is found when dP = (2*kB*T)/(sig),
where sig is the standard deviation of the volume distribution.
For the current trajectories, dmu = 0.5, sig1 = 8 and sig2 = 1e+01.
According to the rule of thumb, given mu1, a good dmu is dmu = 0.7, and
                                given mu2, a good dmu is dmu = 0.5.
Rule of thumb estimates that mu = 0.6 would be optimal (currently, dmu = 0.5)
Computing log of partition functions using pymbar.BAR...
Using -14.07872 for log of partition functions as computed from BAR.
Uncertainty in quantity is 0.04098.
Assuming this is negligible compared to sampling error at individual points.
Computing linear fit parameters (for plotting / comparison)
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Linear Fit Analysis (analytical error)
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Free energy
    -22.89620 +/- 7.63320
Estimated slope                  |  True slope
    0.338431  +/- 0.122713       |  0.200454 
    (1.12 quantiles from true slope)
Estimated dmu                    |  True estimated dmu
    -0.8   +/- 0.3               |  -0.5  
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Computing the maximum likelihood parameters
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Maximum Likelihood Analysis (analytical error)
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Free energy
    -16.10654 +/- 0.76737
Estimated slope                  |  True slope
    0.226822  +/- 0.010828       |  0.200454 
    (2.44 quantiles from true slope)
Estimated dmu                    |  True estimated dmu
    -0.6   +/- 0.0               |  -0.5  
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Computing bootstrapped maximum likelihood parameters... [0/3]
Computing bootstrapped maximum likelihood parameters... [1/3]
Computing bootstrapped maximum likelihood parameters... [2/3]
Computing bootstrapped maximum likelihood parameters... [3/3]
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Maximum Likelihood Analysis (bootstrapped error)
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Free energy
    -16.10654 +/- 0.25745
Estimated slope                  |  True slope
    0.226822  +/- 0.003407       |  0.200454 
    (7.74 quantiles from true slope)
Estimated dmu                    |  True estimated dmu
    -0.6   +/- 0.0               |  -0.5  
==================================================
Calculated slope is 7.7 quantiles from the true slope
