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datasets

Set of datasets to use for the tutorials.

propagate_tmat(tmat, nsteps, start=None)

Markov chain Monte Carlo propagation of transition matrix for nsteps.

Parameters:

  • tmat (ndarray) –

    Transition matrix to propagate.

  • nsteps (int) –

    Number of steps to propagate.

  • start (int, default: None ) –

    Index where to start. If None a random number will be used.

Returns:

  • traj ( ndarray ) –

    Markov chain Monte Carlo state trajectory of given tmat.

Source code in src/msmhelper/utils/datasets.py 
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def propagate_tmat(tmat, nsteps, start=None):
    """Markov chain Monte Carlo propagation of transition matrix for nsteps.

    Parameters
    ----------
    tmat : ndarray
        Transition matrix to propagate.
    nsteps : int
        Number of steps to propagate.
    start : int
        Index where to start. If `None` a random number will be used.

    Returns
    -------
    traj : ndarray
        Markov chain Monte Carlo state trajectory of given tmat.

    """
    if not is_transition_matrix(tmat):
        raise ValueError('tmat needs to be a row-normalized matrix.')

    n_states = len(tmat)
    cummat = np.cumsum(  # enforce exact normalization
        row_normalize_matrix(tmat),
        axis=1,
    )
    cummat_perm = np.tile(np.arange(n_states), (n_states, 1))

    if start is None:
        start = np.random.randint(n_states)

    return _propagate_MCMC(
        cummat=(cummat, cummat_perm),
        start=start,
        steps=nsteps,
    )

hummer15_4state(rate_k, rate_h, nsteps, return_macrotraj=False)

Four state model taken from Hummer and Szabo 15.

Gerhard Hummer and Attila Szabo The Journal of Physical Chemistry B 2015 119 (29), 9029-9037 DOI: 10.1021/jp508375q

Parameters:

  • rate_k (float) –

    Rate between state 1<->2 and 3<->4.

  • rate_h (float) –

    Rate between state 2<->3.

  • nsteps (int) –

    Number of steps to propagate.

  • return_macrotraj (bool, default: False ) –

    If True return a macrotraj where state (1,2) and (3,4) are lumped as well.

Returns:

  • traj ( ndarray ) –

    Markov chain Monte Carlo state trajectory.

  • macrotraj ( ndarray ) –

    Markov chain Monte Carlo macrostate trajectory if macrotraj=True.

Source code in src/msmhelper/utils/datasets.py 
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def hummer15_4state(rate_k, rate_h, nsteps, return_macrotraj=False):
    """Four state model taken from Hummer and Szabo 15.

    Gerhard Hummer and Attila Szabo
    The Journal of Physical Chemistry B 2015 119 (29), 9029-9037
    DOI: [10.1021/jp508375q](https://pubs.acs.org/doi/10.1021/jp508375q)

    Parameters
    ----------
    rate_k : float
        Rate between state 1<->2 and 3<->4.
    rate_h : float
        Rate between state 2<->3.
    nsteps : int
        Number of steps to propagate.
    return_macrotraj : bool, optional
        If `True` return a macrotraj where state (1,2) and (3,4) are lumped
        as well.

    Returns
    -------
    traj : ndarray
        Markov chain Monte Carlo state trajectory.
    macrotraj : ndarray
        Markov chain Monte Carlo macrostate trajectory if `macrotraj=True`.

    """
    return _hummer15_nstate(
        n_states=4,
        rate_k=rate_k,
        rate_h=rate_h,
        nsteps=nsteps,
        return_macrotraj=return_macrotraj,
    )

hummer15_8state(rate_k, rate_h, nsteps, return_macrotraj=False)

Eight state model inspired by Hummer and Szabo 15.

Gerhard Hummer and Attila Szabo The Journal of Physical Chemistry B 2015 119 (29), 9029-9037 DOI: 10.1021/jp508375q

Parameters:

  • rate_k (float) –

    Rate between state 1<->2, 3<->4, 5<->6, 7<->8.

  • rate_h (float) –

    Rate between state 2<->3, 4<->5, 6<->7.

  • nsteps (int) –

    Number of steps to propagate.

  • return_macrotraj (bool, default: False ) –

    If True return a macrotraj where state (1,2), (3,4), (5,6), and (7,8) are lumped as well.

Returns:

  • traj ( ndarray ) –

    Markov chain Monte Carlo state trajectory.

  • macrotraj ( ndarray ) –

    Markov chain Monte Carlo macrostate trajectory if macrotraj=True.

Source code in src/msmhelper/utils/datasets.py 
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def hummer15_8state(rate_k, rate_h, nsteps, return_macrotraj=False):
    """Eight state model inspired by Hummer and Szabo 15.

    Gerhard Hummer and Attila Szabo
    The Journal of Physical Chemistry B 2015 119 (29), 9029-9037
    DOI: [10.1021/jp508375q](https://pubs.acs.org/doi/10.1021/jp508375q)

    Parameters
    ----------
    rate_k : float
        Rate between state 1<->2, 3<->4, 5<->6, 7<->8.
    rate_h : float
        Rate between state 2<->3, 4<->5, 6<->7.
    nsteps : int
        Number of steps to propagate.
    return_macrotraj : bool, optional
        If `True` return a macrotraj where state (1,2), (3,4), (5,6), and (7,8)
        are lumped as well.

    Returns
    -------
    traj : ndarray
        Markov chain Monte Carlo state trajectory.
    macrotraj : ndarray
        Markov chain Monte Carlo macrostate trajectory if `macrotraj=True`.

    """
    return _hummer15_nstate(
        n_states=8,
        rate_k=rate_k,
        rate_h=rate_h,
        nsteps=nsteps,
        return_macrotraj=return_macrotraj,
    )

nagel20_4state(nsteps)

Four state model taken from Nagel et al. 20.

Daniel Nagel, Anna Weber, and Gerhard Stock Journal of Chemical Theory and Computation 2020 16 (12), 7874-7882 DOI: 10.1021/acs.jctc.0c00774

Parameters:

  • nsteps (int) –

    Number of steps to propagate.

Returns:

  • traj ( ndarray ) –

    Markov chain Monte Carlo state trajectory.

Source code in src/msmhelper/utils/datasets.py 
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@_decorate_tmat(_NAGEL20_4STATE_TMAT)
def nagel20_4state(nsteps):
    """Four state model taken from Nagel et al. 20.

    Daniel Nagel, Anna Weber, and Gerhard Stock
    Journal of Chemical Theory and Computation 2020 16 (12), 7874-7882
    DOI: [10.1021/acs.jctc.0c00774](https://doi.org/10.1021/acs.jctc.0c00774)

    Parameters
    ----------
    nsteps : int
        Number of steps to propagate.

    Returns
    -------
    traj : ndarray
        Markov chain Monte Carlo state trajectory.

    """
    return propagate_tmat(_NAGEL20_4STATE_TMAT, nsteps) + 1

nagel20_6state(nsteps)

Six state model taken from Nagel et al. 20.

Daniel Nagel, Anna Weber, and Gerhard Stock Journal of Chemical Theory and Computation 2020 16 (12), 7874-7882 DOI: 10.1021/acs.jctc.0c00774

Parameters:

  • nsteps (int) –

    Number of steps to propagate.

Returns:

  • traj ( ndarray ) –

    Markov chain Monte Carlo state trajectory.

Source code in src/msmhelper/utils/datasets.py 
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@_decorate_tmat(_NAGEL20_6STATE_TMAT)
def nagel20_6state(nsteps):
    """Six state model taken from Nagel et al. 20.

    Daniel Nagel, Anna Weber, and Gerhard Stock
    Journal of Chemical Theory and Computation 2020 16 (12), 7874-7882
    DOI: [10.1021/acs.jctc.0c00774](https://doi.org/10.1021/acs.jctc.0c00774)

    Parameters
    ----------
    nsteps : int
        Number of steps to propagate.

    Returns
    -------
    traj : ndarray
        Markov chain Monte Carlo state trajectory.

    """
    return propagate_tmat(_NAGEL20_6STATE_TMAT, nsteps) + 1