def test_malody(self):
model = Parncutt(segmenter=ManualDSegmenter(),
segment_combiner="cost")
d_corpus = DCorpus(paths=["/Users/dave/malody.abcd"])
model.load_corpus(d_corpus=d_corpus)
advice = model.advise()
print(advice)
# Gold-standard embedded in input file.
hamming_dists = model.evaluate_strike_distance()
print(hamming_dists)
def test_distance_metrics():
parncutt = Parncutt()
d_corpus = DCorpus(corpus_str=TestConstant.A_MAJ_SCALE_SHORT)
parncutt.load_corpus(d_corpus=d_corpus)
complete_rh_advice = parncutt.advise(staff="upper")
complete_rh_advice_len = len(complete_rh_advice)
right_re = re.compile('^>\d+$')
assert right_re.match(complete_rh_advice), "Bad right-hand, upper-staff advice"
rh_advice = parncutt.advise(staff="upper", offset=3, first_digit=4)
short_advice_len = len(rh_advice)
assert complete_rh_advice_len - 3 == short_advice_len, "Bad offset for advise() call"
ff_re = re.compile('^>4\d+$')
assert ff_re.match(rh_advice), "Bad first finger constraint"
rh_advice = parncutt.advise(staff="upper", offset=10, first_digit=1, last_digit=3)
short_advice_len = len(rh_advice)
assert complete_rh_advice_len - 10 == short_advice_len, "Bad offset for advise() call"
ff_re = re.compile('^>1\d+3$')
assert ff_re.match(rh_advice), "Bad first and last finger constraints"
lh_advice = parncutt.advise(staff="lower")
left_re = re.compile('^<\d+$')
assert left_re.match(lh_advice), "Bad left-hand, lower-staff advice"
combo_advice = parncutt.advise(staff="both")
clean_combo_advice = re.sub('[><&]', '', combo_advice)
d_score = d_corpus.d_score_by_index(index=0)
gold_fingering = d_score.abcdf(index=0)
clean_gold_fingering = re.sub('[><&]', '', gold_fingering)
combo_re = re.compile('^>\d+@<\d+$')
assert combo_re.match(combo_advice), "Bad combined advice"
hamming_evaluations = parncutt.evaluate_strike_distance(method="hamming", staff="both")
assert hamming_evaluations[0] > 0, "Undetected Hamming costs"
assert hamming_evaluations[3] == 0, "Bad fish in Hamming barrel"
natural_evaluations = parncutt.evaluate_strike_distance(method="natural", staff="both")
assert natural_evaluations[0] > 0, "Undetected natural costs"
assert natural_evaluations[3] == 0, "Bad fish in natural barrel"
pivot_evaluations = parncutt.evaluate_strike_distance(method="pivot", staff="both")
assert pivot_evaluations[0] > 0, "Undetected pivot costs"
assert pivot_evaluations[3] == 0, "Bad fish in pivot barrel"
#!/usr/bin/env python3
import pprint
from pydactyl.dactyler.Parncutt import Parncutt
from pydactyl.dcorpus.DCorpus import DCorpus
from pydactyl.dcorpus.ManualDSegmenter import ManualDSegmenter
model = Parncutt(segmenter=ManualDSegmenter(), segment_combiner="cost")
# d_corpus = DCorpus(paths=["/Users/dave/malody.abcd"])
d_corpus = DCorpus(paths=["/tmp/malody.abcd"])
model.load_corpus(d_corpus=d_corpus)
advice = model.advise()
print("Best advice: {0}".format(advice))
# Gold-standard embedded in input file.
hamming_dists = model.evaluate_strike_distance()
print("Hamming distance from gold standard: {0}".format(hamming_dists[0]))
suggestions, costs, details = model.generate_advice(staff="upper", k=9)
print("Ranked advice:\n\t{0}".format("\n\t".join(suggestions)))
print("Ranked costs :\n\t{0}".format("\n\t".join(str(x) for x in costs)))
# pp = pprint.PrettyPrinter(width=120)
# pp.pprint(details)