treetime/seq_utils.py
def prof2seq(profile, gtr, sample_from_prof=False, normalize=True):
    """
    Convert profile to sequence and normalize profile across sites.

    Parameters
    ----------

     profile : numpy 2D array
        Profile. Shape of the profile should be (L x a), where L - sequence
        length, a - alphabet size.

     gtr : gtr.GTR
        Instance of the GTR class to supply the sequence alphabet

     collapse_prof : bool
        Whether to convert the profile to the delta-function

    Returns
    -------
     seq : numpy.array
        Sequence as numpy array of length L

     prof_values :  numpy.array
        Values of the profile for the chosen sequence characters (length L)

     idx : numpy.array
        Indices chosen from profile as array of length L
    """

    # normalize profile such that probabilities at each site sum to one
    if normalize:
        tmp_profile, pre=normalize_profile(profile, return_offset=False)
    else:
        tmp_profile = profile

    # sample sequence according to the probabilities in the profile
    # (sampling from cumulative distribution over the different states)
    if sample_from_prof:
        cumdis = tmp_profile.cumsum(axis=1).T
        randnum = np.random.random(size=cumdis.shape[1])
        idx = np.argmax(cumdis>=randnum, axis=0)
    else:
        idx = tmp_profile.argmax(axis=1)
    seq = gtr.alphabet[idx]  # max LH over the alphabet

    prof_values = tmp_profile[np.arange(tmp_profile.shape[0]), idx]

    return seq, prof_values, idx
def prof2seq(profile, gtr, sample_from_prof=False, normalize=True):
    # normalize profile such that probabilities at each site sum to one
    if normalize:
        tmp_profile, pre=normalize_profile(profile, return_offset=False)
    else:
        tmp_profile = profile

    # sample sequence according to the probabilities in the profile
    # (sampling from cumulative distribution over the different states)
    if sample_from_prof:
        cumdis = tmp_profile.cumsum(axis=1).T
        randnum = np.random.random(size=cumdis.shape[1])
        idx = np.argmax(cumdis>=randnum, axis=0)
    else:
        idx = tmp_profile.argmax(axis=1)
    seq = gtr.alphabet[idx]  # max LH over the alphabet

    prof_values = tmp_profile[np.arange(tmp_profile.shape[0]), idx]

    return seq, prof_values, idx
def prof2seq ( profile , gtr , sample_from_prof = False , normalize = True ) : # normalize profile such that probabilities at each site sum to one if normalize : tmp_profile , pre = normalize_profile ( profile , return_offset = False ) else : tmp_profile = profile # sample sequence according to the probabilities in the profile # (sampling from cumulative distribution over the different states) if sample_from_prof : cumdis = tmp_profile . cumsum ( axis = 1 ) . T randnum = np . random . random ( size = cumdis . shape [ 1 ] ) idx = np . argmax ( cumdis >= randnum , axis = 0 ) else : idx = tmp_profile . argmax ( axis = 1 ) seq = gtr . alphabet [ idx ] # max LH over the alphabet prof_values = tmp_profile [ np . arange ( tmp_profile . shape [ 0 ] ) , idx ] return seq , prof_values , idx
Convert profile to sequence and normalize profile across sites.

    Parameters
    ----------

     profile : numpy 2D array
        Profile. Shape of the profile should be (L x a), where L - sequence
        length, a - alphabet size.

     gtr : gtr.GTR
        Instance of the GTR class to supply the sequence alphabet

     collapse_prof : bool
        Whether to convert the profile to the delta-function

    Returns
    -------
     seq : numpy.array
        Sequence as numpy array of length L

     prof_values :  numpy.array
        Values of the profile for the chosen sequence characters (length L)

     idx : numpy.array
        Indices chosen from profile as array of length L
Convert profile to sequence and normalize profile across sites .