How to use the pyemma.util.annotators.shortcut function in pyEMMA

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github markovmodel / PyEMMA / pyemma / msm / estimation / api.py View on Github external
@shortcut('nstates')
def number_of_states(dtrajs, only_used=False):
    r"""returns the number of states in the given trajectories.

    Parameters
    ----------
    dtraj : array_like or list of array_like
        Discretized trajectory or list of discretized trajectories
    only_used = False : boolean
        If False, will return max+1, where max is the largest index used.
        If True, will return the number of states that occur at least once.
    """
    return _number_of_states(dtrajs, only_used=only_used)
github markovmodel / PyEMMA / pyemma / msm / io / api.py View on Github external
@shortcut('save_dtraj')
def save_discrete_trajectory(filename, dtraj):
    r"""Write discrete trajectory to binary file.  

    Parameters
    ---------- 
    filename : str 
        The filename of the discrete state trajectory file. 
        The filename can either contain the full or the 
        relative path to the file.      
    dtraj : array-like of int
        Discrete state trajectory

    See also
    --------
    load_discrete_trajectory
github markovmodel / msmtools / estimators / estimated_msm.py View on Github external
    @shortcut('dtrajs_active')
    def discrete_trajectories_active(self):
        """
        A list of integer arrays with the discrete trajectories mapped to the connectivity mode used.
        For example, for connectivity='largest', the indexes will be given within the connected set.
        Frames that are not in the connected set will be -1.

        """
        self._check_is_estimated()
        # compute connected dtrajs
        self._dtrajs_active = []
        for dtraj in self._dtrajs_full:
            self._dtrajs_active.append(self._full2active[dtraj])

        return self._dtrajs_active
github markovmodel / PyEMMA / pyemma / msm / estimation / api.py View on Github external
@shortcut('connected_cmatrix')
def largest_connected_submatrix(C, directed=True, lcc=None):
    r"""Compute the count matrix on the largest connected set.   
    
    Parameters
    ----------
    C : scipy.sparse matrix 
        Count matrix specifying edge weights.
    directed : bool, optional
       Whether to compute connected components for a directed or
       undirected graph. Default is True
    lcc : (M,) ndarray, optional
       The largest connected set             
       
    Returns
    -------
    C_cc : scipy.sparse matrix
github markovmodel / msmtools / estimators / estimated_hmsm.py View on Github external
    @shortcut('dtrajs_obs')
    def discrete_trajectories_obs(self):
        """
        A list of integer arrays with the discrete trajectories mapped to the observation mode used.
        When using observe_active = True, the indexes will be given on the MSM active set. Frames that are not in the
        observation set will be -1. When observe_active = False, this attribute is identical to
        discrete_trajectories_full

        """
        return self._dtrajs_obs
github markovmodel / msmtools / estimators / estimated_msm.py View on Github external
    @shortcut('dtrajs_full')
    def discrete_trajectories_full(self):
        """
        A list of integer arrays with the original (unmapped) discrete trajectories:

        """
        self._check_is_estimated()
        return self._dtrajs_full
github markovmodel / PyEMMA / pyemma / util / discrete_trajectories.py View on Github external
@shortcut('read_dtraj')
def read_discrete_trajectory(filename):
    """Read discrete trajectory from ascii file.

    The ascii file containing a single column with integer entries is
    read into an array of integers.

    Parameters
    ----------
    filename : str
        The filename of the discrete state trajectory file.
        The filename can either contain the full or the
        relative path to the file.

    Returns
    -------
    dtraj : (M, ) ndarray
github markovmodel / msmtools / estimators / estimated_hmsm.py View on Github external
    @shortcut('dtrajs_full')
    def discrete_trajectories_full(self):
        """
        A list of integer arrays with the original trajectories.

        """
        return self._dtrajs_full
github markovmodel / PyEMMA / pyemma / msm / estimation / api.py View on Github external
@shortcut('histogram')
def count_states(dtrajs):
    r"""returns a histogram count

    Parameters
    ----------
    dtraj : array_like or list of array_like
        Discretized trajectory or list of discretized trajectories

    Returns
    -------
    count : ndarray((n), dtype=int)
        the number of occurrances of each state. n=max+1 where max is the largest state index found.
    """
    return _count_states(dtrajs)
github markovmodel / PyEMMA / pyemma / msm / io / api.py View on Github external
@shortcut('read_dtraj')
def read_discrete_trajectory(filename):
    r"""Read discrete trajectory from ascii file.   
    
    Parameters
    ---------- 
    filename : str
        The filename of the discretized trajectory file. 
        The filename can either contain the full or the 
        relative path to the file.
    
    Returns
    -------
    dtraj : (M, ) ndarray of int
        Discrete state trajectory.

    See also