alchemlyb features parsing submodules for getting raw data from different software packages into common data structures that can be used directly by its :ref:`subsamplers <subsampling>` and :ref:`estimators <estimators>`. Each submodule features at least two functions, namely:
extract_dHdl- Extract the gradient of the Hamiltonian, \frac{dH}{d\lambda}, for each timestep of the sampled state. Required input for :ref:`TI-based estimators <estimators_ti>`.
extract_u_nk- Extract reduced potentials, u_{nk}, for each timestep of the sampled state and all neighboring states. Required input for :ref:`FEP-based estimators <estimators_fep>`.
These functions have a consistent interface across all submodules, often taking a single file as input and any additional parameters required for giving either dHdl or u_nk in standard form.
All components of alchemlyb are designed to work together well with minimal work on the part of the user.
To make this possible, the library deals in a common data structure for each dHdl and u_nk, and all parsers yield these quantities in these standard forms.
The layout of these data structures allow for easy stacking of samples from different simulations while retaining information on where each sample came from using e.g. :py:func:`pandas.concat`.
All parsers yielding dHdl gradients return this as a :py:class:`pandas.DataFrame` with the following structure:
coul vdw time coul-lambda vdw-lambda 0.0 0.0 0.0 10.264125 -0.522539 1.0 0.0 0.0 9.214077 -2.492852 2.0 0.0 0.0 -8.527066 -0.405814 3.0 0.0 0.0 11.544028 -0.358754 ..... ... ... ......... ......... 97.0 1.0 1.0 -10.681702 -18.603644 98.0 1.0 1.0 29.518990 -4.955664 99 0 1.0 1.0 -3.833667 -0.836967 100.0 1.0 1.0 -12.835707 0.786278
This is a multi-index DataFrame, giving time for each sample as the outermost index, and the value of each \lambda from which the sample came as subsequent indexes.
The columns of the DataFrame give the value of \frac{dH}{d\lambda} with respect to each of these separate \lambda parameters.
For datasets that sample with only a single \lambda parameter, then the DataFrame will feature only a single column perhaps like:
fep time fep-lambda 0.0 0.0 10.264125 1.0 0.0 9.214077 2.0 0.0 -8.527066 3.0 0.0 11.544028 ..... ... ......... 97.0 1.0 -10.681702 98.0 1.0 29.518990 99 0 1.0 -3.833667 100.0 1.0 -12.835707
All parsers yielding u_nk reduced potentials return this as a :py:class:`pandas.DataFrame` with the following structure:
(0.0, 0.0) (0.25, 0.0) (0.5, 0.0) ... (1.0, 1.0) time coul-lambda vdw-lambda 0.0 0.0 0.0 -22144.50 -22144.24 -22143.98 -21984.81 1.0 0.0 0.0 -21985.24 -21985.10 -21984.96 -22124.26 2.0 0.0 0.0 -22124.58 -22124.47 -22124.37 -22230.61 3.0 1.0 0.1 -22230.65 -22230.63 -22230.62 -22083.04 ..... ... ... ......... ......... ......... ... ......... 97.0 1.0 1.0 -22082.29 -22082.54 -22082.79 -22017.42 98.0 1.0 1.0 -22087.57 -22087.76 -22087.94 -22135.15 99.0 1.0 1.0 -22016.69 -22016.93 -22017.17 -22057.68 100.0 1.0 1.0 -22137.19 -22136.51 -22135.83 -22101.26
This is a multi-index DataFrame, giving time for each sample as the outermost index, and the value of each \lambda from which the sample came as subsequent indexes.
The columns of the DataFrame give the value of u_{nk} for each set of \lambda parameters values were recorded for.
Column labels are the values of the \lambda parameters as a tuple in the same order as they appear in the multi-index.
For datasets that sample only a single \lambda parameter, then the DataFrame will feature only a single index in addition to time, with the values of \lambda for which reduced potentials were recorded given as column labels:
0.0 0.25 0.5 ... 1.0 time fep-lambda 0.0 0.0 -22144.50 -22144.24 -22143.98 -21984.81 1.0 0.0 -21985.24 -21985.10 -21984.96 -22124.26 2.0 0.0 -22124.58 -22124.47 -22124.37 -22230.61 3.0 1.0 -22230.65 -22230.63 -22230.62 -22083.04 ..... ... ......... ......... ......... ... ......... 97.0 1.0 -22082.29 -22082.54 -22082.79 -22017.42 98.0 1.0 -22087.57 -22087.76 -22087.94 -22135.15 99.0 1.0 -22016.69 -22016.93 -22017.17 -22057.68 100.0 1.0 -22137.19 -22136.51 -22135.83 -22101.26
Throughout alchemlyb, energy quantities such as dHdl or u_nk are given in units of k_B T.
Also, although parsers will extract timestamps from input data, these are taken as-is and the library does not have any awareness of units for these.
Keep this in mind when doing, e.g. :ref:`subsampling <subsampling>`.
alchemlyb tries to provide parser functions for as many simulation packages as possible. See the documentation for the package you are using for more details on parser usage, including the assumptions parsers make and suggestions for how output data should be structured for ease of use:
.. currentmodule:: alchemlyb.parsing
.. autosummary::
:toctree: parsing
gmx
amber