asigalov61
commited on
Commit
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bfc143d
1
Parent(s):
710f8d3
Upload TMIDIX.py
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TMIDIX.py
CHANGED
@@ -7034,6 +7034,397 @@ def transpose_escore_notes_to_pitch(escore_notes,
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7034 |
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7035 |
###################################################################################
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7036 |
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7037 |
# This is the end of the TMIDI X Python module
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7038 |
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7039 |
###################################################################################
|
|
|
7034 |
|
7035 |
###################################################################################
|
7036 |
|
7037 |
+
CHORDS_TYPES = ['WHITE', 'BLACK', 'UNKNOWN', 'MIXED WHITE', 'MIXED BLACK', 'MIXED GRAY']
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7038 |
+
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7039 |
+
###################################################################################
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7040 |
+
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7041 |
+
def tones_chord_type(tones_chord,
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7042 |
+
return_chord_type_index=True,
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7043 |
+
use_filtered_chords=True
|
7044 |
+
):
|
7045 |
+
|
7046 |
+
WN = WHITE_NOTES
|
7047 |
+
BN = BLACK_NOTES
|
7048 |
+
MX = WHITE_NOTES + BLACK_NOTES
|
7049 |
+
|
7050 |
+
if use_filtered_chords:
|
7051 |
+
CHORDS = ALL_CHORDS_FILTERED
|
7052 |
+
|
7053 |
+
else:
|
7054 |
+
CHORDS = ALL_CHORDS_SORTED
|
7055 |
+
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7056 |
+
tones_chord = sorted(tones_chord)
|
7057 |
+
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7058 |
+
ctype = 'UNKNOWN'
|
7059 |
+
|
7060 |
+
if tones_chord in CHORDS:
|
7061 |
+
|
7062 |
+
if sorted(set(tones_chord) & set(WN)) == tones_chord:
|
7063 |
+
ctype = 'WHITE'
|
7064 |
+
|
7065 |
+
elif sorted(set(tones_chord) & set(BN)) == tones_chord:
|
7066 |
+
ctype = 'BLACK'
|
7067 |
+
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7068 |
+
if len(tones_chord) > 1 and sorted(set(tones_chord) & set(MX)) == tones_chord:
|
7069 |
+
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7070 |
+
if len(sorted(set(tones_chord) & set(WN))) == len(sorted(set(tones_chord) & set(BN))):
|
7071 |
+
ctype = 'MIXED GRAY'
|
7072 |
+
|
7073 |
+
elif len(sorted(set(tones_chord) & set(WN))) > len(sorted(set(tones_chord) & set(BN))):
|
7074 |
+
ctype = 'MIXED WHITE'
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7075 |
+
|
7076 |
+
elif len(sorted(set(tones_chord) & set(WN))) < len(sorted(set(tones_chord) & set(BN))):
|
7077 |
+
ctype = 'MIXED BLACK'
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7078 |
+
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7079 |
+
if return_chord_type_index:
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7080 |
+
return CHORDS_TYPES.index(ctype)
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7081 |
+
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7082 |
+
else:
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7083 |
+
return ctype
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7084 |
+
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7085 |
+
###################################################################################
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7086 |
+
|
7087 |
+
def tone_type(tone,
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7088 |
+
return_tone_type_index=True
|
7089 |
+
):
|
7090 |
+
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7091 |
+
tone = tone % 12
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7092 |
+
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7093 |
+
if tone in BLACK_NOTES:
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7094 |
+
if return_tone_type_index:
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7095 |
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return CHORDS_TYPES.index('BLACK')
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7096 |
+
else:
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7097 |
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return "BLACK"
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7098 |
+
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7099 |
+
else:
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7100 |
+
if return_tone_type_index:
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7101 |
+
return CHORDS_TYPES.index('WHITE')
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7102 |
+
else:
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7103 |
+
return "WHITE"
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7104 |
+
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7105 |
+
###################################################################################
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7106 |
+
|
7107 |
+
def lists_sym_differences(src_list, trg_list):
|
7108 |
+
return list(set(src_list) ^ set(trg_list))
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7109 |
+
|
7110 |
+
###################################################################################
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7111 |
+
|
7112 |
+
def lists_differences(long_list, short_list):
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7113 |
+
return list(set(long_list) - set(short_list))
|
7114 |
+
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7115 |
+
###################################################################################
|
7116 |
+
|
7117 |
+
def find_best_tones_chord(src_tones_chords,
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7118 |
+
trg_tones_chords,
|
7119 |
+
find_longest=True
|
7120 |
+
):
|
7121 |
+
|
7122 |
+
not_seen_trg_chords = []
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7123 |
+
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7124 |
+
max_len = 0
|
7125 |
+
|
7126 |
+
for tc in trg_tones_chords:
|
7127 |
+
if sorted(tc) in src_tones_chords:
|
7128 |
+
not_seen_trg_chords.append(sorted(tc))
|
7129 |
+
max_len = max(max_len, len(tc))
|
7130 |
+
|
7131 |
+
if not not_seen_trg_chords:
|
7132 |
+
max_len = len(max(trg_tones_chords, key=len))
|
7133 |
+
not_seen_trg_chords = trg_tones_chords
|
7134 |
+
|
7135 |
+
if find_longest:
|
7136 |
+
return random.choice([c for c in not_seen_trg_chords if len(c) == max_len])
|
7137 |
+
|
7138 |
+
else:
|
7139 |
+
return random.choice(not_seen_trg_chords)
|
7140 |
+
|
7141 |
+
###################################################################################
|
7142 |
+
|
7143 |
+
def find_matching_tones_chords(tones_chord,
|
7144 |
+
matching_chord_length=-1,
|
7145 |
+
match_chord_type=True,
|
7146 |
+
use_filtered_chords=True
|
7147 |
+
):
|
7148 |
+
|
7149 |
+
if use_filtered_chords:
|
7150 |
+
CHORDS = ALL_CHORDS_FILTERED
|
7151 |
+
else:
|
7152 |
+
CHORDS = ALL_CHORDS_SORTED
|
7153 |
+
|
7154 |
+
tones_chord = sorted(tones_chord)
|
7155 |
+
|
7156 |
+
tclen = len(tones_chord)
|
7157 |
+
|
7158 |
+
tctype = tones_chord_type(tones_chord, use_filtered_chords=use_filtered_chords)
|
7159 |
+
|
7160 |
+
matches = []
|
7161 |
+
|
7162 |
+
for tc in CHORDS:
|
7163 |
+
|
7164 |
+
if matching_chord_length == -1:
|
7165 |
+
if len(tc) > tclen:
|
7166 |
+
if sorted(lists_intersections(tc, tones_chord)) == tones_chord:
|
7167 |
+
if match_chord_type:
|
7168 |
+
if tones_chord_type(tc, use_filtered_chords=use_filtered_chords) == tctype:
|
7169 |
+
tcdiffs = lists_differences(tc, tones_chord)
|
7170 |
+
if all(tone_type(d) == tctype % 3 for d in tcdiffs):
|
7171 |
+
matches.append(tc)
|
7172 |
+
else:
|
7173 |
+
matches.append(tc)
|
7174 |
+
|
7175 |
+
else:
|
7176 |
+
|
7177 |
+
if len(tc) == max(tclen, matching_chord_length):
|
7178 |
+
if sorted(lists_intersections(tc, tones_chord)) == tones_chord:
|
7179 |
+
if match_chord_type:
|
7180 |
+
if tones_chord_type(tc, use_filtered_chords=use_filtered_chords) == tctype:
|
7181 |
+
tcdiffs = lists_differences(tc, tones_chord)
|
7182 |
+
if all(tone_type(d) == tctype % 3 for d in tcdiffs):
|
7183 |
+
matches.append(tc)
|
7184 |
+
else:
|
7185 |
+
matches.append(tc)
|
7186 |
+
|
7187 |
+
return sorted(matches, key=len)
|
7188 |
+
|
7189 |
+
###################################################################################
|
7190 |
+
|
7191 |
+
def adjust_list_of_values_to_target_average(list_of_values,
|
7192 |
+
trg_avg,
|
7193 |
+
min_value,
|
7194 |
+
max_value
|
7195 |
+
):
|
7196 |
+
|
7197 |
+
filtered_values = [value for value in list_of_values if min_value <= value <= max_value]
|
7198 |
+
|
7199 |
+
if not filtered_values:
|
7200 |
+
return list_of_values
|
7201 |
+
|
7202 |
+
current_avg = sum(filtered_values) / len(filtered_values)
|
7203 |
+
scale_factor = trg_avg / current_avg
|
7204 |
+
|
7205 |
+
adjusted_values = [value * scale_factor for value in filtered_values]
|
7206 |
+
|
7207 |
+
total_difference = trg_avg * len(filtered_values) - sum(adjusted_values)
|
7208 |
+
adjustment_per_value = total_difference / len(filtered_values)
|
7209 |
+
|
7210 |
+
final_values = [value + adjustment_per_value for value in adjusted_values]
|
7211 |
+
|
7212 |
+
while abs(sum(final_values) / len(final_values) - trg_avg) > 1e-6:
|
7213 |
+
total_difference = trg_avg * len(final_values) - sum(final_values)
|
7214 |
+
adjustment_per_value = total_difference / len(final_values)
|
7215 |
+
final_values = [value + adjustment_per_value for value in final_values]
|
7216 |
+
|
7217 |
+
final_values = [round(value) for value in final_values]
|
7218 |
+
|
7219 |
+
adjusted_values = copy.deepcopy(list_of_values)
|
7220 |
+
|
7221 |
+
j = 0
|
7222 |
+
|
7223 |
+
for i in range(len(adjusted_values)):
|
7224 |
+
if min_value <= adjusted_values[i] <= max_value:
|
7225 |
+
adjusted_values[i] = final_values[j]
|
7226 |
+
j += 1
|
7227 |
+
|
7228 |
+
return adjusted_values
|
7229 |
+
|
7230 |
+
###################################################################################
|
7231 |
+
|
7232 |
+
def adjust_escore_notes_to_average(escore_notes,
|
7233 |
+
trg_avg,
|
7234 |
+
min_value=1,
|
7235 |
+
max_value=4000,
|
7236 |
+
times_index=1,
|
7237 |
+
durs_index=2,
|
7238 |
+
score_is_delta=False,
|
7239 |
+
return_delta_scpre=False
|
7240 |
+
):
|
7241 |
+
if score_is_delta:
|
7242 |
+
delta_escore_notes = copy.deepcopy(escore_notes)
|
7243 |
+
|
7244 |
+
else:
|
7245 |
+
delta_escore_notes = delta_score_notes(escore_notes)
|
7246 |
+
|
7247 |
+
times = [[e[times_index], e[durs_index]] for e in delta_escore_notes]
|
7248 |
+
|
7249 |
+
filtered_values = [value for value in times if min_value <= value[0] <= max_value]
|
7250 |
+
|
7251 |
+
if not filtered_values:
|
7252 |
+
return escore_notes
|
7253 |
+
|
7254 |
+
current_avg = sum([v[0] for v in filtered_values]) / len([v[0] for v in filtered_values])
|
7255 |
+
scale_factor = trg_avg / current_avg
|
7256 |
+
|
7257 |
+
adjusted_values = [[value[0] * scale_factor, value[1] * scale_factor] for value in filtered_values]
|
7258 |
+
|
7259 |
+
total_difference = trg_avg * len([v[0] for v in filtered_values]) - sum([v[0] for v in adjusted_values])
|
7260 |
+
adjustment_per_value = total_difference / len(filtered_values)
|
7261 |
+
|
7262 |
+
final_values = [[value[0] + adjustment_per_value, value[1] + adjustment_per_value] for value in adjusted_values]
|
7263 |
+
|
7264 |
+
while abs(sum([v[0] for v in final_values]) / len(final_values) - trg_avg) > 1e-6:
|
7265 |
+
total_difference = trg_avg * len(final_values) - sum([v[0] for v in final_values])
|
7266 |
+
adjustment_per_value = total_difference / len(final_values)
|
7267 |
+
final_values = [[value[0] + adjustment_per_value, value[1] + adjustment_per_value] for value in final_values]
|
7268 |
+
|
7269 |
+
final_values = [[round(value[0]), round(value[1])] for value in final_values]
|
7270 |
+
|
7271 |
+
adjusted_delta_score = copy.deepcopy(delta_escore_notes)
|
7272 |
+
|
7273 |
+
j = 0
|
7274 |
+
|
7275 |
+
for i in range(len(adjusted_delta_score)):
|
7276 |
+
if min_value <= adjusted_delta_score[i][1] <= max_value:
|
7277 |
+
adjusted_delta_score[i][times_index] = final_values[j][0]
|
7278 |
+
adjusted_delta_score[i][durs_index] = final_values[j][1]
|
7279 |
+
j += 1
|
7280 |
+
|
7281 |
+
adjusted_escore_notes = delta_score_to_abs_score(adjusted_delta_score)
|
7282 |
+
|
7283 |
+
if return_delta_scpre:
|
7284 |
+
return adjusted_delta_score
|
7285 |
+
|
7286 |
+
else:
|
7287 |
+
return adjusted_escore_notes
|
7288 |
+
|
7289 |
+
###################################################################################
|
7290 |
+
|
7291 |
+
def harmonize_enhanced_melody_score_notes_to_ms_SONG(escore_notes,
|
7292 |
+
melody_velocity=-1,
|
7293 |
+
melody_channel=3,
|
7294 |
+
melody_patch=40,
|
7295 |
+
melody_base_octave=4,
|
7296 |
+
harmonized_tones_chords_velocity=-1,
|
7297 |
+
harmonized_tones_chords_channel=0,
|
7298 |
+
harmonized_tones_chords_patch=0
|
7299 |
+
):
|
7300 |
+
|
7301 |
+
harmonized_tones_chords = harmonize_enhanced_melody_score_notes(escore_notes)
|
7302 |
+
|
7303 |
+
harm_escore_notes = []
|
7304 |
+
|
7305 |
+
time = 0
|
7306 |
+
|
7307 |
+
for i, note in enumerate(escore_notes):
|
7308 |
+
|
7309 |
+
time = note[1]
|
7310 |
+
dur = note[2]
|
7311 |
+
ptc = note[4]
|
7312 |
+
|
7313 |
+
if melody_velocity == -1:
|
7314 |
+
vel = int(110 + ((ptc % 12) * 1.5))
|
7315 |
+
else:
|
7316 |
+
vel = melody_velocity
|
7317 |
+
|
7318 |
+
harm_escore_notes.append(['note', time, dur, melody_channel, ptc, vel, melody_patch])
|
7319 |
+
|
7320 |
+
for t in harmonized_tones_chords[i]:
|
7321 |
+
|
7322 |
+
ptc = (melody_base_octave * 12) + t
|
7323 |
+
|
7324 |
+
if harmonized_tones_chords_velocity == -1:
|
7325 |
+
vel = int(80 + ((ptc % 12) * 1.5))
|
7326 |
+
else:
|
7327 |
+
vel = harmonized_tones_chords_velocity
|
7328 |
+
|
7329 |
+
harm_escore_notes.append(['note', time, dur, harmonized_tones_chords_channel, ptc, vel, harmonized_tones_chords_patch])
|
7330 |
+
|
7331 |
+
return sorted(harm_escore_notes, key=lambda x: (x[1], -x[4], x[6]))
|
7332 |
+
|
7333 |
+
###################################################################################
|
7334 |
+
|
7335 |
+
def check_and_fix_pitches_chord(pitches_chord,
|
7336 |
+
use_filtered_chords=True
|
7337 |
+
):
|
7338 |
+
|
7339 |
+
pitches_chord = sorted(pitches_chord, reverse=True)
|
7340 |
+
|
7341 |
+
if use_filtered_chords:
|
7342 |
+
CHORDS = ALL_CHORDS_FILTERED
|
7343 |
+
else:
|
7344 |
+
CHORDS = ALL_CHORDS_SORTED
|
7345 |
+
|
7346 |
+
tones_chord = sorted(set([p % 12 for p in pitches_chord]))
|
7347 |
+
|
7348 |
+
if tones_chord not in CHORDS:
|
7349 |
+
|
7350 |
+
if len(tones_chord) == 2:
|
7351 |
+
|
7352 |
+
tones_counts = Counter([p % 12 for p in pitches_chord]).most_common()
|
7353 |
+
|
7354 |
+
if tones_counts[0][1] > 1:
|
7355 |
+
tones_chord = [tones_counts[0][0]]
|
7356 |
+
elif tones_counts[1][1] > 1:
|
7357 |
+
tones_chord = [tones_counts[1][0]]
|
7358 |
+
else:
|
7359 |
+
tones_chord = [pitches_chord[0] % 12]
|
7360 |
+
|
7361 |
+
if len(tones_chord) > 2:
|
7362 |
+
|
7363 |
+
tones_chord_combs = [list(comb) for i in range(len(tones_chord)-2, 0, -1) for comb in combinations(tones_chord, i+1)]
|
7364 |
+
|
7365 |
+
for co in tones_chord_combs:
|
7366 |
+
if co in CHORDS:
|
7367 |
+
tones_chord = co
|
7368 |
+
break
|
7369 |
+
|
7370 |
+
new_pitches_chord = []
|
7371 |
+
|
7372 |
+
for p in pitches_chord:
|
7373 |
+
|
7374 |
+
if p % 12 in tones_chord:
|
7375 |
+
new_pitches_chord.append(p)
|
7376 |
+
|
7377 |
+
return sorted(new_pitches_chord, reverse=True)
|
7378 |
+
|
7379 |
+
###################################################################################
|
7380 |
+
|
7381 |
+
ALL_CHORDS_TRANS = [[0], [0, 4], [0, 4, 7], [0, 4, 8], [0, 5], [0, 6], [0, 7], [0, 8], [1], [1, 5],
|
7382 |
+
[1, 5, 9], [1, 6], [1, 7], [1, 8], [1, 9], [2], [2, 6], [2, 6, 10], [2, 7],
|
7383 |
+
[2, 8], [2, 9], [2, 10], [3], [3, 7], [3, 7, 11], [3, 8], [3, 9], [3, 10],
|
7384 |
+
[3, 11], [4], [4, 7], [4, 7, 11], [4, 8], [4, 9], [4, 10], [4, 11], [5],
|
7385 |
+
[5, 9], [5, 10], [5, 11], [6], [6, 10], [6, 11], [7], [7, 11], [8], [9], [10],
|
7386 |
+
[11]]
|
7387 |
+
|
7388 |
+
###################################################################################
|
7389 |
+
|
7390 |
+
def minkowski_distance(x, y, p=3):
|
7391 |
+
|
7392 |
+
if len(x) != len(y):
|
7393 |
+
return -1
|
7394 |
+
|
7395 |
+
distance = 0
|
7396 |
+
|
7397 |
+
for i in range(len(x)):
|
7398 |
+
distance += abs(x[i] - y[i]) ** p
|
7399 |
+
|
7400 |
+
return distance ** (1 / p)
|
7401 |
+
|
7402 |
+
###################################################################################
|
7403 |
+
|
7404 |
+
def dot_product(x, y):
|
7405 |
+
return sum(xi * yi for xi, yi in zip(x, y))
|
7406 |
+
|
7407 |
+
def norm(vector):
|
7408 |
+
return sum(xi ** 2 for xi in vector) ** 0.5
|
7409 |
+
|
7410 |
+
def cosine_similarity(x, y):
|
7411 |
+
|
7412 |
+
if len(x) != len(y):
|
7413 |
+
return -1
|
7414 |
+
|
7415 |
+
dot_prod = dot_product(x, y)
|
7416 |
+
|
7417 |
+
norm_x = norm(x)
|
7418 |
+
norm_y = norm(y)
|
7419 |
+
|
7420 |
+
if norm_x == 0 or norm_y == 0:
|
7421 |
+
return 0.0
|
7422 |
+
|
7423 |
+
else:
|
7424 |
+
return dot_prod / (norm_x * norm_y)
|
7425 |
+
|
7426 |
+
###################################################################################
|
7427 |
+
|
7428 |
# This is the end of the TMIDI X Python module
|
7429 |
|
7430 |
###################################################################################
|