# Moving from `pymbar`

version 3¶

Pymbar v4.0 contains several changes to improve the API longer term. This, however, breaks the API used in 3.x and previous versions.

The main changes include:

Making various estimators return dictionaries, not tuples, making it easier to return optional information requested at call time.

Standardizing on snake_case for function names.

Making the built-in solvers work to have an interface closer to like

`scipy`

solvers.

## Snake case¶

Previous version of pymbar had mixed cases in functions. We have standardized on snake case, and tried to make the method names that do similar things more consistent. Specific changes include:

`getFreeEnergyDifferences`

is now`compute_free_energy_differences`

`computeExpectations`

is now`compute_expectations`

`computeMultipleExpectations`

is now`compute_multiple_expectations`

`computePerturbedFreeEnergies`

is now`compute_perturbed_free_energies`

`computeEntropyAndEnthalpy`

is now`compute_entropy_and_enthalpy`

In the submodule timeseries:

`statisticalInefficiency`

is now`statistical_inefficiency`

`statisticalInefficiencyMltiple`

is now`statistical_inefficiency_multiple`

`integratedAutocorrelationTime`

is now`integrated_autocorrelation_time`

`normalizedFluctuationCorrelationFunction`

is now`normalized_fluctuation_correlation_function`

`normalizedFluctuationCorrelationFunctionMultiple`

is now`normalized_fluctuation_correlation_function_multiple`

`subsampleCorrelatedData`

is now`subsample_correlated_data`

`detectEquilibration`

is now`detect_equilibration`

`statisticalInefficiency_fft`

is now`statistical_inefficiency_fft`

`detectEquilibration_binary_search`

is now`detect_equilibration_binary_search`

Additionally, the other estimators such as the Bennett Acceptance
Ratio and exponential averaging/Zwanzig equation have different, more
consistent, call signatures. All other estimators are now in the
`other_estimators`

module.

`BAR`

is now`bar`

`EXP`

is now`exp`

`EXPGauss`

is now`exp_gauss`

`PMF`

is now`FEP`

and is greatly expanded (see Free energy surfaces with pymbar).

## More consistent return functionality¶

Previously, different pymbar functions returned different information as tuples. This became problematic when different functions returned different types of information or different numbers of results. We have thus consolidated on an API where all functions return a dictionary.

As an example of both API changes of API, a short bit of code that would load in data and calculate free energies, instead of being

```
mbar = MBAR(u_kn, N_k)
results, errors = mbar.getFreeEnergyDifferences()
print(results[0])
print(errors[0])
```

Would now be written as:

```
mbar = MBAR(u_kn, N_k)
results = mbar.compute_free_energy_differences()
print(results['Delta_f'])
print(results['dDelta_f'])
```

Other estimators including `bar`

and `exp`

also use a dictionary for return data.

The `pymbar.timeseries`

submodule return patterns have *not* changed
in 4.0, however, and one should refer to the individual function
documentations for these return patterns.

```
results = bar(w_F, w_R)
print(f'Free energy difference is {results['Delta_f']:.3f} +- {results['Delta_f']:.3f} kT')
and:
```

```
results = exp(w_F)
print(f"Forward free energy difference is {results['Delta_f']:.3f} +- {results['dDelta_f']:.3f} kT)
results = exp(w_R)
print(f"Reverse free energy difference is {results['Delta_f']:.3f} +- {results['dDelta_f']:.3f} kT)
```

## Simulation output¶

Previously, `pymbar`

send all messages to standard out when verbose
was set to `True`

. `pymbar`

now uses the logging module to output
this information. If you wish to set messages, even if the verbose is
set to `True`

, you will need to turn on logging for your script by
importing the logging module, and adding the lines:

```
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
```

`pymbar`

generally uses the logging levels `info`

for information
that previously was set to standard out. Note that for a given method
to produce extensive information, even with logging, the verbose flag
still needs to be set to true.

## Free energy surfaces¶

Previously, `pymbar`

had a method `PMF`

that estimated a free
energy from a series of umbrella samples using a histogram
approach. This was sematically problematin in two ways. First, the
term PMF (potential of mean force) is somewhat of an ambiguous term,
as the potential of mean force has some dependence on the coordinate
system in which the mean force is calculated. Since `pymbar`

does
not calculate free energies by integration of mean force, this caused
some comfusion. To be clearer, we now have renamed the class
`FES`

, for “free energy surface”.

The inclusion of a PMF function also created some confusion where some authors referred to MBAR as a method to calculate a free energy surface. MBAR can only be used to take biased samples an estimate the unbiased weight of each sample. In order to calculate a free energy surface, one must also find a way to take the set of discrete weighted samples and calculate a continous potential of mean force: see Shirts and Ferguson [1] for a further discussion of the separation of these two distinct tasks in the construction of free energy surfaces. The pymbar code more cleanly separates the calculation of biasing weights associated with umbrella samples, and the estimation of the free energy surface.

For more information on the options for computing free energy surfaces with the code, please see: Free energy surfaces with pymbar.

## Acceleration¶

Previous version of `pymbar`

include acceleration using explict C++
inner loops. The C++ interface has become out of date. `pymbar`

optimization routines are now accelerated with `jax`

. This provides
approximately a 2x speed up when performed on most CPUs, and
additional acceleration when a GPU can be detected (pymbar does not
install the appropriate GPU libraries). `jax`

will be installed when
`pymbar`

in installed via conda, but `pymbar`

will function with
or without `jax`

installed if there are issues with the JAX configuration.

## Other changes¶

- Additional changes not affecting the API:
Removed legacy old_mbar.py support.

Moved testing framework to pytest, added significant numbers of tests.

Improved code linting using black

Bootstrapping for errors in free energies and expectations is now supported; see Strategies for solution for more information.

Added a bar_overlap function to find overlap when using just bar

Fixed an error in computing expectations of small numbers.

Improved automated adaptive choice of samplers; see Strategies for solution for more information.

Many instances of code cleanup.

Improved docstring documentation.