def test_good_rules():
parncutt = Parncutt()
parncutt.segment_combiner(method="cost")
for id in subcosts:
d_corpus = DCorpus(corpus_str=TestConstant.PARNCUTT_HUMAN_FRAGMENT[id])
parncutt.load_corpus(d_corpus=d_corpus)
if id == 'B':
suggestions, costs, details = parncutt.generate_advice(staff="upper", cycle=4, k=20)
else:
suggestions, costs, details = parncutt.generate_advice(staff="upper", last_digit=last_digit[id], k=30)
details_for_sugg = dict()
for i in range(len(details)):
details_for_sugg[suggestions[i]] = details[i][0] # 0 index because we only have one segment
# parncutt.report_on_advice(suggestions, costs, details)
for gold_sugg in subcosts[id]:
assert gold_sugg in details_for_sugg, \
"Missing suggestion {0} in {1}".format(gold_sugg, id)
for rule in subcosts[id][gold_sugg]:
# if rule == 'bl1':
# continue
gold_cost = subcosts[id][gold_sugg][rule]
cost = details_for_sugg[gold_sugg][rule]
assert cost == gold_cost, \
"Bad {0} cost for {1} in {2}: {3} should be {4}".format(rule, gold_sugg, id, cost, gold_cost)
def test_fingering_counts():
parncutt = Parncutt(pruning_method="none")
parncutt.segment_combiner(method="cost")
d_corpus = DCorpus(corpus_str=TestConstant.FOUR_NOTES)
parncutt.load_corpus(d_corpus=d_corpus)
suggestions, costs, details = parncutt.generate_advice(staff="upper", k=2)
assert parncutt.last_segment_pruned_count() == 320, "Bad none pruning on open-ended problem"
def test_four_note_example(self):
parncutt = Parncutt(segmenter=ManualDSegmenter(), segment_combiner="cost")
# parncutt.segment_combiner(method="cost")
d_corpus = DCorpus(corpus_str=TestConstant.FOUR_NOTES)
parncutt.load_corpus(d_corpus=d_corpus)
suggestions, costs, details = parncutt.generate_advice(staff="upper", k=2)
self.assertEqual(len(suggestions), 2, "No loops in that dog in top ten")
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_pivot_alignment():
parncutt = Parncutt()
d_corpus = DCorpus(corpus_str=TestConstant.A_MAJ_SCALE_SHORT)
parncutt.load_corpus(d_corpus=d_corpus)
evaluations = parncutt.evaluate_pivot_alignment(staff="both")
# for he in hamming_evaluations:
# print(he)
assert evaluations[0] > 0, "Undetected pivot alignment costs"
assert evaluations[3] == 0, "Bad fish in pivot alignment barrel"
def test_a_segment_cost():
parncutt = Parncutt(segment_combiner="cost")
d_corpus = DCorpus(corpus_str=TestConstant.PARNCUTT_HUMAN_FRAGMENT['A'])
parncutt.load_corpus(d_corpus=d_corpus)
abcdf = ">24342313"
cost, details = parncutt.segment_advice_cost(abcdf)
# print("")
# print(abcdf)
# print("Cost: {0}".format(cost))
# for det in details:
# print(det)
assert cost == 6, "Bad segment cost calculation"
def test_cycles():
parncutt = Parncutt()
parncutt.segment_combiner(method="cost")
d_corpus = DCorpus(corpus_str=TestConstant.FOUR_NOTES)
parncutt.load_corpus(d_corpus=d_corpus)
suggestions, costs, details = parncutt.generate_advice(staff="upper", cycle=4, k=2)
assert len(suggestions) == 2, "No loops in that dog in top ten"
# parncutt.report_on_advice(suggestions, costs, details)
d_corpus = DCorpus(corpus_str=TestConstant.PARNCUTT_HUMAN_FRAGMENT['B'])
parncutt.load_corpus(d_corpus=d_corpus)
suggestions, costs, details = parncutt.generate_advice(staff="upper", cycle=4, k=16)
assert len(suggestions) == 16, "There should be 16 cyclic fingerings!"
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"
def test_reentry():
parncutt = Parncutt()
# We cannot use the longer example A_MAJ_SCALE because the gold standard fingering
# requires hand repositionings not allowed by the Parncutt model. This reinforces the need
# for segmentation and also (maybe) the need for a more inclusive option for Parncutt where
# all paths are possible but some are just very expensive, as we have in Sayegh.
d_corpus = DCorpus(corpus_str=TestConstant.A_MAJ_SCALE_SHORT)
parncutt.load_corpus(d_corpus=d_corpus)
reentry_hamming_evals = parncutt.evaluate_strike_reentry(method="hamming", staff="upper", gold_indices=[2, 3])
# Note we are not picking Beringer for the real gold standard because Beringer and Parncutt agree
# on the fingering for this scale.
# for rhe in reentry_hamming_evals:
# print("RHE:{0}".format(rhe))
assert reentry_hamming_evals[0] > 0, "Undetected upper Hamming reentry costs"
assert reentry_hamming_evals[1] == 0, "Bad fish in upper-staff Hamming reentry barrel"
reentry_hamming_evals = parncutt.evaluate_strike_reentry(method="hamming", staff="both", gold_indices=[2, 3])
# for rhe in reentry_hamming_evals:
# print("RHE:{0}".format(rhe))
assert reentry_hamming_evals[0] > 0, "Undetected both-staff Hamming reentry costs"
assert reentry_hamming_evals[1] == 0, "Bad fish in both-staff Hamming reentry barrel"
hamming_score = reentry_hamming_evals[0]
reentry_natural_evals = parncutt.evaluate_strike_reentry(method="natural", staff="both", gold_indices=[2, 3])
# for rne in reentry_natural_evals:
# print("RNE:{0}".format(rne))
assert reentry_natural_evals[0] > 0, "Undetected natural reentry costs"
assert reentry_natural_evals[1] == 0, "Bad fish in natural reentry barrel"
natural_score = reentry_natural_evals[0]
assert natural_score > hamming_score, "Reentry: Natural <= Hamming"
reentry_pivot_evals = parncutt.evaluate_strike_reentry(method="pivot", staff="both", gold_indices=[2, 3])
# for rpe in reentry_pivot_evals:
# print("RPE:{0}".format(rpe))
assert reentry_pivot_evals[0] > 0, "Undetected pivot reentry costs"
assert reentry_pivot_evals[1] == 0, "Bad fish in pivot reentry barrel"
pivot_score = reentry_pivot_evals[0]
assert natural_score < pivot_score, "Reentry: Natural >= Pivot"
def test_sma_lar():
parncutt = Parncutt()
parncutt.segment_combiner(method="cost")
for id in sma_lar_sums:
d_corpus = DCorpus(corpus_str=TestConstant.PARNCUTT_HUMAN_FRAGMENT[id])
parncutt.load_corpus(d_corpus=d_corpus)
if id == 'B':
suggestions, costs, details = parncutt.generate_advice(staff="upper", cycle=4, k=20)
else:
suggestions, costs, details = parncutt.generate_advice(staff="upper", last_digit=last_digit[id], k=20)
details_for_sugg = dict()
for i in range(len(details)):
details_for_sugg[suggestions[i]] = details[i][0] # 0 index because we only have one segment
# parncutt.report_on_advice(suggestions, costs, details)
for gold_sugg in sma_lar_sums[id]:
assert gold_sugg in details_for_sugg,\
"Missing suggestion {0} in {1}".format(gold_sugg, id)
sma = details_for_sugg[gold_sugg]['sma']
lar = details_for_sugg[gold_sugg]['lar']
assert sma + lar == sma_lar_sums[id][gold_sugg],\
"Bad sma + lar total for {0} in {1}".format(gold_sugg, id)
def test_parncutt_edges():
parncutt = Parncutt()
d_corpus = DCorpus(corpus_str=TestConstant.ONE_NOTE)
parncutt.load_corpus(d_corpus=d_corpus)
upper_rh_advice = parncutt.advise(staff="upper")
right_re = re.compile('^>\d$')
assert right_re.match(upper_rh_advice), "Bad one-note, right-hand, upper-staff advice"
# both_advice = parncutt.advise(staff="both")
# both_re = re.compile('^>\d@$')
# assert both_re.match(both_advice), "Bad one-note, segregated, both-staff advice"
parncutt = Parncutt()
d_corpus = DCorpus(corpus_str=TestConstant.ONE_BLACK_NOTE_PER_STAFF)
parncutt.load_corpus(d_corpus=d_corpus)
upper_advice = parncutt.advise(staff="upper")
right_re = re.compile('^>2$')
assert right_re.match(upper_advice), "Bad black-note, upper-staff advice"
lower_advice = parncutt.advise(staff="lower")
left_re = re.compile('^<2$')
assert left_re.match(lower_advice), "Bad black-note, upper-staff advice"
both_advice = parncutt.advise(staff="both")
both_re = re.compile('^>2@<2$')
assert both_re.match(both_advice), "Bad black-note, both-staff advice"
lower_advice = parncutt.advise(staff="lower", first_digit=3)
lower_re = re.compile('^<3$')
assert lower_re.match(lower_advice), "Bad preset black-note, both-staff advice"
parncutt = Parncutt()
d_corpus = DCorpus(corpus_str=TestConstant.TWO_WHITE_NOTES_PER_STAFF)
parncutt.load_corpus(d_corpus=d_corpus)
upper_advice = parncutt.advise(staff="upper")
right_re = re.compile('^>\d\d$')
assert right_re.match(upper_advice), "Bad two white-note, upper-staff advice"
upper_advice = parncutt.advise(staff="upper", first_digit=2, last_digit=4)
right_re = re.compile('^>24$')
assert right_re.match(upper_advice), "Bad preset two white-note, upper-staff advice"
#!/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)
alpha = d_score.nltk_alpha(ids=[16, 17], staff=staff, common_id=3)
kappa, pairs = d_score.cohens_kappa(16, 17, staff=staff, common_id=3)
aggregate_pairs(interpolation2_pairs, pairs)
print("Interpol 2 {} staff{} Section {}.1 Kappa: {}".format(
staff, plural, score_index + 1, kappa))
print("Interpol 2 {} staff{} Section {}.1 Alpha: {}".format(
staff, plural, score_index + 1, alpha))
score_index += 1
print_pairs("Annotation 1 pairs:", annotation_pairs)
print_pairs("Interpolation 1 pairs:", interpolation_pairs)
print_pairs("Interpolation 2 pairs:", interpolation2_pairs)
exit(0)
parncutt = Parncutt()
d_corpus = DCorpus(paths=phrase_paths)
parncutt.load_corpus(d_corpus=d_corpus)
jacobs = Jacobs()
jacobs.load_corpus(d_corpus=d_corpus)
justin = Badgerow()
justin.load_corpus(d_corpus=d_corpus)
model_years = {'Parncutt': '1997', 'Jacobs': '2001', 'Badgerow': '2019'}
models = [parncutt, jacobs, justin]
for model in models:
model_name = str(type(model)).split('.')[-1]
model_name = model_name[0:-2]
model_version = model.version()
version_str = ".".join(map(str, model_version))
authority = model_name + ' Model (' + model_years[model_name] + ')'
for i in range(len(phrase_ids)):