import parangonar as pa
import partitura as pt

perf_match, groundtruth_alignment, score_match = pt.load_match(
    filename= pa.EXAMPLE,
    create_score=True
)

# compute note arrays from the loaded score and performance
pna_match = perf_match[0].note_array()
sna_match = score_match[0].note_array()

# match the notes in the note arrays --------------------- DualDTWNoteMatcher
sdm = pa.AutomaticNoteMatcher()
pred_alignment = sdm(sna_match, 
                     pna_match,
                     verbose_time=True)

# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
    precision, recall, f_score = pa.fscore_alignments(pred_alignment, 
                                                      groundtruth_alignment, 
                                                      alignment_type)
    print('Evaluate ',alignment_type)
    print('Precision: ',format(precision, '.3f'),
          'Recall ',format(recall, '.3f'),
          'F-Score ',format(f_score, '.3f'))
    print('------------------')




# this matcher requires grace note info
sna_match = score_match[0].note_array(include_grace_notes=True)

# match the notes in the note arrays --------------------- DualDTWNoteMatcher
sdm = pa.DualDTWNoteMatcher()
pred_alignment = sdm(sna_match, 
                     pna_match,
                     process_ornaments=False,
                     score_part=score_match[0]) # if a score part is passed, ornaments can be handled seperately

# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
    precision, recall, f_score = pa.fscore_alignments(pred_alignment, 
                                                      groundtruth_alignment, 
                                                      alignment_type)
    print('Evaluate ',alignment_type)
    print('Precision: ',format(precision, '.3f'),
          'Recall ',format(recall, '.3f'),
          'F-Score ',format(f_score, '.3f'))
    print('------------------')

import parangonar as pa
import partitura as pt

score = pt.load_score(filename= 'path/to/score_file')
performance = pt.load_performance_midi(filename= 'path/to/midi_file')

# compute note arrays from the loaded score and performance
pna = performance.note_array()
sna = score.note_array()

# match the notes in the note arrays
sdm = pa.AutomaticNoteMatcher()
pred_alignment = sdm(sna, pna)

import parangonar as pa
import partitura as pt

perf_match, groundtruth_alignment, score_match = pt.load_match(
    filename= pa.EXAMPLE,
    create_score=True
)

# compute note arrays from the loaded score and performance
pna_match = perf_match.note_array()
sna_match = score_match.note_array()

# compute synthetic anchor points every 4 beats
nodes = pa.match.node_array(score_match[0], 
                   perf_match[0], 
                   groundtruth_alignment,
                   node_interval=4)

# match the notes in the note arrays
apdm = pa.AnchorPointNoteMatcher()
pred_alignment = apdm(sna_match, 
                     pna_match,
                     nodes)

# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
    precision, recall, f_score = pa.fscore_alignments(pred_alignment, 
                                                      groundtruth_alignment, 
                                                      alignment_type)
    print('Evaluate ',alignment_type)
    print('Precision: ',format(precision, '.3f'),
          'Recall ',format(recall, '.3f'),
          'F-Score ',format(f_score, '.3f'))
    print('------------------')

import parangonar as pa
import partitura as pt

perf_match, groundtruth_alignment, score_match = pt.load_match(
    filename= pa.EXAMPLE,
    create_score=True
)

# compute note arrays from the loaded score and performance
pna_match = perf_match[0].note_array()
# this matcher requires grace note info
sna_match = score_match[0].note_array(include_grace_notes=True)

# set up the matcher using the score information: OnlineTransformerMatcher / OnlinePureTransformerMatcher
matcher = pa.OnlinePureTransformerMatcher(sna_match)

# the "offline" method loops over all notes in the performance and calls the "online" method for each one.
pred_alignment = matcher.offline(pna_match)

# compute f-score and print the results
print('------------------')
types = ['match','insertion', 'deletion']
for alignment_type in types:
    precision, recall, f_score = pa.fscore_alignments(pred_alignment, 
                                                      groundtruth_alignment, 
                                                      alignment_type)
    print('Evaluate ',alignment_type)
    print('Precision: ',format(precision, '.3f'),
          'Recall ',format(recall, '.3f'),
          'F-Score ',format(f_score, '.3f'))
    print('------------------')

import parangonar as pa
import partitura as pt

perf_match, alignment, score_match = pt.load_match(
    filename= pa.EXAMPLE,
    create_score=True
)
pna_match = perf_match.note_array()
sna_match = score_match.note_array()

# show or save plot of note alignment
pa.plot_alignment(pna_match,
                sna_match,
                alignment,s
                save_file = False)

# or plot the performance and score as piano rolls given a reference: 
# we can encode errors if given ground truth
# Blue lines indicate correct matches, red lines incorrect ones.
pa.plot_alignment_comparison(pna_match, sna_match, 
                         pred_alignment, groundtruth_alignment)

import partitura as pt
import parangonar as pa

# export a note alignment for visualization with parangonada:
# https://sildater.github.io/parangonada/
pa.match.save_parangonada_csv(alignment, 
                            performance_data,
                            score_data,
                            outdir="path/to/dir")


# import a corrected note alignment from parangonada:
# https://sildater.github.io/parangonada/
alignment = pt.io.importparangonada.load_parangonada_alignment(filename= 'path/to/note_alignment.csv')

# load note alignments of the asap dataset: 
# https://github.com/CPJKU/asap-dataset/tree/note_alignments
alignment = pt.io.importparangonada.load_alignment_from_ASAP(filename= 'path/to/note_alignment.tsv')