How to use the pomegranate.State function in pomegranate

if vpaths:
        alignment = get_multiple_alignment_of_repeats_from_reads(vpaths)
        transitions, emissions = build_profile_hmm_pseudocounts_for_alignment(settings.MAX_ERROR_RATE, alignment)
    else:
        transitions, emissions = build_profile_hmm_for_repeats(patterns, settings.MAX_ERROR_RATE)
    matches = [m for m in emissions.keys() if m.startswith('M')]

    last_end = None
    for repeat in range(copies):
        insert_states = []
        match_states = []
        delete_states = []
        for i in range(len(matches) + 1):
            insert_distribution = DiscreteDistribution(emissions['I%s' % i])
            insert_states.append(State(insert_distribution, name='I%s_%s' % (i, repeat)))

        for i in range(1, len(matches) + 1):
            match_distribution = DiscreteDistribution(emissions['M%s' % i])
            match_states.append(State(match_distribution, name='M%s_%s' % (str(i), repeat)))

        for i in range(1, len(matches) + 1):
            delete_states.append(State(None, name='D%s_%s' % (str(i), repeat)))

        unit_start = State(None, name='unit_start_%s' % repeat)
        unit_end = State(None, name='unit_end_%s' % repeat)
        model.add_states(insert_states + match_states + delete_states + [unit_start, unit_end])
        n = len(delete_states)-1

        if repeat > 0:
            model.add_transition(last_end, unit_start, 1)
        else:

def build_reference_repeat_finder_hmm(patterns, copies=1):
    pattern = patterns[0]
    model = Model(name="HMM Model")
    insert_distribution = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})

    last_end = None
    start_random_matches = State(insert_distribution, name='start_random_matches')
    end_random_matches = State(insert_distribution, name='end_random_matches')
    model.add_states([start_random_matches, end_random_matches])
    for repeat in range(copies):
        insert_states = []
        match_states = []
        delete_states = []
        for i in range(len(pattern) + 1):
            insert_states.append(State(insert_distribution, name='I%s_%s' % (i, repeat)))

        for i in range(len(pattern)):
            distribution_map = dict({'A': 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.01})
            distribution_map[pattern[i]] = 0.97
            match_states.append(State(DiscreteDistribution(distribution_map), name='M%s_%s' % (str(i + 1), repeat)))

        for i in range(len(pattern)):
            delete_states.append(State(None, name='D%s_%s' % (str(i + 1), repeat)))

        unit_start = State(None, name='unit_start_%s' % repeat)
        unit_end = State(None, name='unit_end_%s' % repeat)
        model.add_states(insert_states + match_states + delete_states + [unit_start, unit_end])
        last = len(delete_states)-1

        if repeat > 0:
            model.add_transition(last_end, unit_start, 0.5)

def make_hmm_model(emission_mat, transition_probs):
    model = pomegranate.HiddenMarkovModel('ndf')

    ictal_emissions    = {i:emission_mat[1,i] for i in range(emission_mat.shape[1])}
    baseline_emissions = {i:emission_mat[0,i] for i in range(emission_mat.shape[1])}

    ictal    = pomegranate.State(pomegranate.DiscreteDistribution(ictal_emissions   ), name = '1')
    baseline = pomegranate.State(pomegranate.DiscreteDistribution(baseline_emissions), name = '0')

    model.add_state(ictal)
    model.add_state(baseline)

    model.add_transition( model.start, ictal, 0.05 )
    model.add_transition( model.start, baseline, 99.95)

    model.add_transition( baseline, baseline, transition_probs[0,0] )
    model.add_transition( baseline, ictal,    transition_probs[0,1]  )
    model.add_transition( ictal, ictal   ,    transition_probs[1,1] )
    model.add_transition( ictal, baseline,    transition_probs[1,0]  )

    model.bake(verbose=False )
    return model

def test_example_pomegranate(self):
        """
        This example is taken from https://pomegranate.readthedocs.io/en/latest/HiddenMarkovModel.html
        """

        from pomegranate import DiscreteDistribution, State, HiddenMarkovModel
        d1 = DiscreteDistribution({'A': 0.35, 'C': 0.20, 'G': 0.05, 'T': 0.40})
        d2 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})
        d3 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})

        s1 = State(d1, name="s1")
        s2 = State(d2, name="s2")
        s3 = State(d3, name="s3")

        model = HiddenMarkovModel(name='example')
        model.add_states([s1, s2, s3])
        model.add_transition(model.start, s1, 0.90)
        model.add_transition(model.start, s2, 0.10)
        model.add_transition(s1, s1, 0.80)
        model.add_transition(s1, s2, 0.20)
        model.add_transition(s2, s2, 0.90)
        model.add_transition(s2, s3, 0.10)
        model.add_transition(s3, s3, 0.70)
        model.add_transition(s3, model.end, 0.30)
        model.bake()

        answer = model.log_probability(list('ACGACTATTCGAT'))
        expected = -22.73896159971087

def load_segmentation_model(modeldata):
    model = HiddenMarkovModel('model')

    states = {}
    for s in modeldata:
        if len(s['emission']) == 1:
            emission = NormalDistribution(*s['emission'][0][:2])
        else:
            weights = np.array([w for _, _, w in s['emission']])
            dists = [NormalDistribution(mu, sigma)
                     for mu, sigma, _ in s['emission']]
            emission = GeneralMixtureModel(dists, weights=weights)
        state = State(emission, name=s['name'])

        states[s['name']] = state
        model.add_state(state)
        if 'start_prob' in s:
            model.add_transition(model.start, state, s['start_prob'])

    for s in modeldata:
        current = states[s['name']]
        for nextstate, prob in s['transition']:
            model.add_transition(current, states[nextstate], prob)

    model.bake()

    return model

def test_example_pomegranate(self):
        """
        This example is taken from https://pomegranate.readthedocs.io/en/latest/HiddenMarkovModel.html
        """

        from pomegranate import DiscreteDistribution, State, HiddenMarkovModel
        d1 = DiscreteDistribution({'A': 0.35, 'C': 0.20, 'G': 0.05, 'T': 0.40})
        d2 = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})
        d3 = DiscreteDistribution({'A': 0.10, 'C': 0.40, 'G': 0.40, 'T': 0.10})

        s1 = State(d1, name="s1")
        s2 = State(d2, name="s2")
        s3 = State(d3, name="s3")

        model = HiddenMarkovModel(name='example')
        model.add_states([s1, s2, s3])
        model.add_transition(model.start, s1, 0.90)
        model.add_transition(model.start, s2, 0.10)
        model.add_transition(s1, s1, 0.80)
        model.add_transition(s1, s2, 0.20)
        model.add_transition(s2, s2, 0.90)
        model.add_transition(s2, s3, 0.10)
        model.add_transition(s3, s3, 0.70)
        model.add_transition(s3, model.end, 0.30)
        model.bake()

        answer = model.log_probability(list('ACGACTATTCGAT'))
        expected = -22.73896159971087

def get_suffix_matcher_hmm(pattern):
    model = Model(name="Suffix Matcher HMM Model")
    insert_distribution = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})
    insert_states = []
    match_states = []
    delete_states = []
    hmm_name = 'suffix'
    for i in range(len(pattern) + 1):
        insert_states.append(State(insert_distribution, name='I%s_%s' % (i, hmm_name)))

    for i in range(len(pattern)):
        distribution_map = dict({'A': 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.01})
        distribution_map[pattern[i]] = 0.97
        match_states.append(State(DiscreteDistribution(distribution_map), name='M%s_%s' % (str(i + 1), hmm_name)))

    for i in range(len(pattern)):
        delete_states.append(State(None, name='D%s_%s' % (str(i + 1), hmm_name)))

    unit_start = State(None, name='suffix_start_%s' % hmm_name)
    unit_end = State(None, name='suffix_end_%s' % hmm_name)
    model.add_states(insert_states + match_states + delete_states + [unit_start, unit_end])
    last = len(delete_states)-1

    model.add_transition(model.start, unit_start, 1)

def build_reference_repeat_finder_hmm(patterns, copies=1):
    pattern = patterns[0]
    model = Model(name="HMM Model")
    insert_distribution = DiscreteDistribution({'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25})

    last_end = None
    start_random_matches = State(insert_distribution, name='start_random_matches')
    end_random_matches = State(insert_distribution, name='end_random_matches')
    model.add_states([start_random_matches, end_random_matches])
    for repeat in range(copies):
        insert_states = []
        match_states = []
        delete_states = []
        for i in range(len(pattern) + 1):
            insert_states.append(State(insert_distribution, name='I%s_%s' % (i, repeat)))

        for i in range(len(pattern)):
            distribution_map = dict({'A': 0.01, 'C': 0.01, 'G': 0.01, 'T': 0.01})
            distribution_map[pattern[i]] = 0.97
            match_states.append(State(DiscreteDistribution(distribution_map), name='M%s_%s' % (str(i + 1), repeat)))

        for i in range(len(pattern)):
            delete_states.append(State(None, name='D%s_%s' % (str(i + 1), repeat)))

for repeat in range(copies):
        insert_states = []
        match_states = []
        delete_states = []
        for i in range(len(matches) + 1):
            insert_distribution = DiscreteDistribution(emissions['I%s' % i])
            insert_states.append(State(insert_distribution, name='I%s_%s' % (i, repeat)))

        for i in range(1, len(matches) + 1):
            match_distribution = DiscreteDistribution(emissions['M%s' % i])
            match_states.append(State(match_distribution, name='M%s_%s' % (str(i), repeat)))

        for i in range(1, len(matches) + 1):
            delete_states.append(State(None, name='D%s_%s' % (str(i), repeat)))

        unit_start = State(None, name='unit_start_%s' % repeat)
        unit_end = State(None, name='unit_end_%s' % repeat)
        model.add_states(insert_states + match_states + delete_states + [unit_start, unit_end])
        n = len(delete_states)-1

        if repeat > 0:
            model.add_transition(last_end, unit_start, 1)
        else:
            model.add_transition(model.start, unit_start, 1)

        if repeat == copies - 1:
            model.add_transition(unit_end, model.end, 1)

        model.add_transition(unit_start, match_states[0], transitions['unit_start']['M1'])
        model.add_transition(unit_start, delete_states[0], transitions['unit_start']['D1'])
        model.add_transition(unit_start, insert_states[0], transitions['unit_start']['I0'])