Utilities : `pymbar.utils`¶

These functions are some miscellaneous functions used by other parts of the pymbar library.

exception pymbar.utils.BoundsError¶: Could not determine bounds on free energy

exception pymbar.utils.ConvergenceError¶: Convergence could not be achieved.

exception pymbar.utils.DataError¶: Data is inconsistent.

exception pymbar.utils.ParameterError¶: An error in the input parameters has been detected.

exception pymbar.utils.TypeCastPerformanceWarning¶

pymbar.utils.check_w_normalized(W, N_k, tolerance=0.0001)¶

Check the weight matrix W is properly normalized. The sum over N should be 1, and the sum over k by N_k should aslo be 1

Parameters

Wnp.ndarray, shape=(N, K), dtype=’float’: The normalized weight matrix for snapshots and states. W[n, k] is the weight of snapshot n in state k.
N_knp.ndarray, shape=(K), dtype=’int’: N_k[k] is the number of samples from state k.
tolerancefloat, optional, default=1.0e-4: Tolerance for checking equality of sums

Returns

NoneNoneType: Returns a None object if test passes, otherwise raises a ParameterError with appropriate message if W is not normalized within tolerance.

pymbar.utils.ensure_type(val, dtype, ndim, name, length=None, can_be_none=False, shape=None, warn_on_cast=True, add_newaxis_on_deficient_ndim=False)¶

Typecheck the size, shape and dtype of a numpy array, with optional casting.

Parameters

val{np.ndaraay, None}: The array to check
dtype{nd.dtype, str}: The dtype you’d like the array to have
ndimint: The number of dimensions you’d like the array to have
namestr: name of the array. This is used when throwing exceptions, so that we can describe to the user which array is messed up.
lengthint, optional: How long should the array be?
can_be_nonebool: Is val == None acceptable?
shapetuple, optional: What should be shape of the array be? If the provided tuple has Nones in it, those will be semantically interpreted as matching any length in that dimension. So, for example, using the shape spec (None, None, 3) will ensure that the last dimension is of length three without constraining the first two dimensions
warn_on_castbool, default=True: Raise a warning when the dtypes don’t match and a cast is done.
add_newaxis_on_deficient_ndimbool, default=True: Add a new axis to the beginining of the array if the number of dimensions is deficient by one compared to your specification. For instance, if you’re trying to get out an array of ndim == 3, but the user provides an array of shape == (10, 10), a new axis will be created with length 1 in front, so that the return value is of shape (1, 10, 10).

Returns

typechecked_valnp.ndarray, None: If val=None and can_be_none=True, then this will return None. Otherwise, it will return val (or a copy of val). If the dtype wasn’t right, it’ll be casted to the right shape. If the array was not C-contiguous, it’ll be copied as well.

Notes

The returned value will always be C-contiguous.

pymbar.utils.kln_to_kn(kln, N_k=None, cleanup=False)¶

Convert KxKxN_max array to KxN max array

if self.N is not initialized, it will be here.

Parameters

u_klnnp.ndarray, float, shape=(KxLxN_max)
N_k (optional)np.array: the N_k matrix from the previous formatting form
cleanup (optional)bool: optional command to clean up, since u_kln can get very large

pymbar.utils.kn_to_n(kn, N_k=None, cleanup=False)¶

Convert KxN_max array to N array

Parameters

u_kn: np.ndarray, float, shape=(KxN_max)
N_k (optional)np.array: the N_k matrix from the previous formatting form
cleanup (optional)bool: optional command to clean up, since u_kln can get very large

Utilities : pymbar.utils¶

Utilities : `pymbar.utils`¶