# Path: alfpy/utils/distance.py
# class Distance(object):
#     def __getitem__(self, seqnum):
#     def get_disttypes(cls):
#     def set_disttype(self, disttype):
#     def __init__(self, vector, disttype):
#     def pwdist_euclid_squared(self, seq1idx, seq2idx):
#     def pwdist_euclid_norm(self, seq1idx, seq2idx):
#     def pwdist_google(self, seq1idx, seq2idx):


import numpy as np
from .utils import distance
    from .utils.seqrecords import main
    from . import word_pattern
    from .utils import distmatrix
        pos1 = word_positions[i - 1]
        pos2 = word_positions[i]
        pos = pos2 - pos1
        l.append(pos)
    return np.mean(l), np.std(l)


def create_vector(seqcount, pattern):
    """Compute a matrix of sequence-representing RTD vectors

    Args:
        seqcount (int): number of sequences
        pattern (obj: word_pattern.Pattern)

    Returns:
        ndarray: matrix of RTD vectors
                 (shape: number of seqs, doubled number of words)

    """
    words = pattern.pat_list
    data = np.zeros(shape=(seqcount, len(words) * 2))
    for wordidx in range(len(words)):
        for seqidx in pattern.pos_list[wordidx]:
            word_positions = pattern.pos_list[wordidx][seqidx]
            mean, std = calc_rtd(word_positions)
            data[seqidx, wordidx * 2] = mean
            data[seqidx, wordidx * 2 + 1] = std
    return data



# Path: alfpy/utils/distance.py
# class Distance(object):
#     def __getitem__(self, seqnum):
#     def get_disttypes(cls):
#     def set_disttype(self, disttype):
#     def __init__(self, vector, disttype):
#     def pwdist_euclid_squared(self, seq1idx, seq2idx):
#     def pwdist_euclid_norm(self, seq1idx, seq2idx):
#     def pwdist_google(self, seq1idx, seq2idx):


# Path: alfpy/word_rtd.py
import numpy as np
from .utils import distance
    from .utils.seqrecords import main
    from . import word_pattern
    from .utils import distmatrix

        pos1 = word_positions[i - 1]
        pos2 = word_positions[i]
        pos = pos2 - pos1
        l.append(pos)
    return np.mean(l), np.std(l)


def create_vector(seqcount, pattern):
    """Compute a matrix of sequence-representing RTD vectors

    Args:
        seqcount (int): number of sequences
        pattern (obj: word_pattern.Pattern)

    Returns:
        ndarray: matrix of RTD vectors
                 (shape: number of seqs, doubled number of words)

    """
    words = pattern.pat_list
    data = np.zeros(shape=(seqcount, len(words) * 2))
    for wordidx in range(len(words)):
        for seqidx in pattern.pos_list[wordidx]:
            word_positions = pattern.pos_list[wordidx][seqidx]
            mean, std = calc_rtd(word_positions)
            data[seqidx, wordidx * 2] = mean
            data[seqidx, wordidx * 2 + 1] = std
    return data



class Distance(distance.Distance):