Rst-wham: Difference between revisions
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::<code>/path/to/timeseries/file loc_win_min spring [correl time] [temperature]</code> | ::<code>/path/to/timeseries/file loc_win_min spring [correl time] [temperature]</code> | ||
This first field is the name of one of the time series files (more on this in a moment). The second field, loc_win_min, is the location of the minimum of the biasing potential for this simulation, a floating point number. The third field, spring, is the spring constant for the biasing potential used in this simulation, assuming the biasing potential is of the format | |||
Many simulation packages, including TINKER, AMBER, and CHARMM, do not include the [1/2] when they specify spring constants for their restraint terms. This is a common source of error (I'd love to change my code to match the other packages' behavior, but then experienced users who don't read the manual would get messed up). Also, the units for the spring constant must match those for the time series. So, if your time series is a distance recorded in Ångstroms, the spring constant must be in kcal/mol-Å2. AMBER users should take care when using angular restraints: the specification and output of angles is in degrees, but AMBER's spring constants use kcal/mol-rad2. | |||
The fourth argument ("correl time") specifies the decorrelation time for your time series, in units of time steps. It is only used when generating fake data sets for Monte Carlo bootstrap error analysis, where it modulates the number of points per fake data set. This argument is optional, and is ignored if you don't do error analysis. If you're doing multiple temperatures but not bootstrapping, set it to any integer value as a placeholder, and it'll be ignored. See section for more discussion about how to do bootstrapping. | The fourth argument ("correl time") specifies the decorrelation time for your time series, in units of time steps. It is only used when generating fake data sets for Monte Carlo bootstrap error analysis, where it modulates the number of points per fake data set. This argument is optional, and is ignored if you don't do error analysis. If you're doing multiple temperatures but not bootstrapping, set it to any integer value as a placeholder, and it'll be ignored. See section for more discussion about how to do bootstrapping. | ||