Revision 5:c5482b2a4bdd
| Vamp-2chroma.ipynb | ||
|---|---|---|
| 13 | 13 |
}, |
| 14 | 14 |
{
|
| 15 | 15 |
"cell_type": "code", |
| 16 |
"execution_count": 3,
|
|
| 16 |
"execution_count": 2,
|
|
| 17 | 17 |
"metadata": {
|
| 18 | 18 |
"collapsed": false |
| 19 | 19 |
}, |
| ... | ... | |
| 22 | 22 |
"name": "stderr", |
| 23 | 23 |
"output_type": "stream", |
| 24 | 24 |
"text": [ |
| 25 |
"/usr/lib/python3.4/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
|
|
| 25 |
"/usr/lib/python2.7/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
|
|
| 26 | 26 |
" warnings.warn('Could not import scikits.samplerate. '\n"
|
| 27 | 27 |
] |
| 28 | 28 |
} |
| ... | ... | |
| 33 | 33 |
}, |
| 34 | 34 |
{
|
| 35 | 35 |
"cell_type": "code", |
| 36 |
"execution_count": 12,
|
|
| 36 |
"execution_count": 3,
|
|
| 37 | 37 |
"metadata": {
|
| 38 | 38 |
"collapsed": true |
| 39 | 39 |
}, |
| ... | ... | |
| 44 | 44 |
}, |
| 45 | 45 |
{
|
| 46 | 46 |
"cell_type": "code", |
| 47 |
"execution_count": 13,
|
|
| 47 |
"execution_count": 4,
|
|
| 48 | 48 |
"metadata": {
|
| 49 | 49 |
"collapsed": true |
| 50 | 50 |
}, |
| ... | ... | |
| 55 | 55 |
}, |
| 56 | 56 |
{
|
| 57 | 57 |
"cell_type": "code", |
| 58 |
"execution_count": 2,
|
|
| 58 |
"execution_count": 5,
|
|
| 59 | 59 |
"metadata": {
|
| 60 | 60 |
"collapsed": false |
| 61 | 61 |
}, |
| ... | ... | |
| 160 | 160 |
" 'vamp-test-plugin:vamp-test-plugin-freq']" |
| 161 | 161 |
] |
| 162 | 162 |
}, |
| 163 |
"execution_count": 2,
|
|
| 163 |
"execution_count": 5,
|
|
| 164 | 164 |
"metadata": {},
|
| 165 | 165 |
"output_type": "execute_result" |
| 166 | 166 |
} |
| ... | ... | |
| 171 | 171 |
}, |
| 172 | 172 |
{
|
| 173 | 173 |
"cell_type": "code", |
| 174 |
"execution_count": 4,
|
|
| 174 |
"execution_count": 6,
|
|
| 175 | 175 |
"metadata": {
|
| 176 | 176 |
"collapsed": true |
| 177 | 177 |
}, |
| ... | ... | |
| 182 | 182 |
}, |
| 183 | 183 |
{
|
| 184 | 184 |
"cell_type": "code", |
| 185 |
"execution_count": 5,
|
|
| 185 |
"execution_count": 7,
|
|
| 186 | 186 |
"metadata": {
|
| 187 | 187 |
"collapsed": true |
| 188 | 188 |
}, |
| ... | ... | |
| 193 | 193 |
}, |
| 194 | 194 |
{
|
| 195 | 195 |
"cell_type": "code", |
| 196 |
"execution_count": 6,
|
|
| 196 |
"execution_count": 8,
|
|
| 197 | 197 |
"metadata": {
|
| 198 | 198 |
"collapsed": false |
| 199 | 199 |
}, |
| ... | ... | |
| 204 | 204 |
}, |
| 205 | 205 |
{
|
| 206 | 206 |
"cell_type": "code", |
| 207 |
"execution_count": 7,
|
|
| 207 |
"execution_count": 9,
|
|
| 208 | 208 |
"metadata": {
|
| 209 | 209 |
"collapsed": false |
| 210 | 210 |
}, |
| ... | ... | |
| 215 | 215 |
"44100" |
| 216 | 216 |
] |
| 217 | 217 |
}, |
| 218 |
"execution_count": 7,
|
|
| 218 |
"execution_count": 9,
|
|
| 219 | 219 |
"metadata": {},
|
| 220 | 220 |
"output_type": "execute_result" |
| 221 | 221 |
} |
| ... | ... | |
| 226 | 226 |
}, |
| 227 | 227 |
{
|
| 228 | 228 |
"cell_type": "code", |
| 229 |
"execution_count": 8,
|
|
| 229 |
"execution_count": 10,
|
|
| 230 | 230 |
"metadata": {
|
| 231 | 231 |
"collapsed": false |
| 232 | 232 |
}, |
| ... | ... | |
| 237 | 237 |
}, |
| 238 | 238 |
{
|
| 239 | 239 |
"cell_type": "code", |
| 240 |
"execution_count": 20,
|
|
| 240 |
"execution_count": 11,
|
|
| 241 | 241 |
"metadata": {
|
| 242 | 242 |
"collapsed": true |
| 243 | 243 |
}, |
| ... | ... | |
| 248 | 248 |
}, |
| 249 | 249 |
{
|
| 250 | 250 |
"cell_type": "code", |
| 251 |
"execution_count": 9,
|
|
| 251 |
"execution_count": 12,
|
|
| 252 | 252 |
"metadata": {
|
| 253 | 253 |
"collapsed": false |
| 254 | 254 |
}, |
| ... | ... | |
| 272 | 272 |
" 0.15729675, 0.1570534 ]], dtype=float32))}" |
| 273 | 273 |
] |
| 274 | 274 |
}, |
| 275 |
"execution_count": 9,
|
|
| 275 |
"execution_count": 12,
|
|
| 276 | 276 |
"metadata": {},
|
| 277 | 277 |
"output_type": "execute_result" |
| 278 | 278 |
} |
| ... | ... | |
| 283 | 283 |
}, |
| 284 | 284 |
{
|
| 285 | 285 |
"cell_type": "code", |
| 286 |
"execution_count": 21,
|
|
| 286 |
"execution_count": 13,
|
|
| 287 | 287 |
"metadata": {
|
| 288 | 288 |
"collapsed": false |
| 289 | 289 |
}, |
| ... | ... | |
| 307 | 307 |
" 5.40428482e-05, 4.73081054e-05, 1.41351460e-03]], dtype=float32))}" |
| 308 | 308 |
] |
| 309 | 309 |
}, |
| 310 |
"execution_count": 21,
|
|
| 310 |
"execution_count": 13,
|
|
| 311 | 311 |
"metadata": {},
|
| 312 | 312 |
"output_type": "execute_result" |
| 313 | 313 |
} |
| ... | ... | |
| 318 | 318 |
}, |
| 319 | 319 |
{
|
| 320 | 320 |
"cell_type": "code", |
| 321 |
"execution_count": 22,
|
|
| 321 |
"execution_count": 14,
|
|
| 322 | 322 |
"metadata": {
|
| 323 | 323 |
"collapsed": false |
| 324 | 324 |
}, |
| ... | ... | |
| 329 | 329 |
}, |
| 330 | 330 |
{
|
| 331 | 331 |
"cell_type": "code", |
| 332 |
"execution_count": 14,
|
|
| 332 |
"execution_count": 15,
|
|
| 333 | 333 |
"metadata": {
|
| 334 | 334 |
"collapsed": false |
| 335 | 335 |
}, |
| ... | ... | |
| 337 | 337 |
{
|
| 338 | 338 |
"data": {
|
| 339 | 339 |
"text/plain": [ |
| 340 |
"<matplotlib.image.AxesImage at 0x7f6bde77d588>"
|
|
| 340 |
"<matplotlib.image.AxesImage at 0x7f1b00878790>"
|
|
| 341 | 341 |
] |
| 342 | 342 |
}, |
| 343 |
"execution_count": 14,
|
|
| 343 |
"execution_count": 15,
|
|
| 344 | 344 |
"metadata": {},
|
| 345 | 345 |
"output_type": "execute_result" |
| 346 | 346 |
}, |
| ... | ... | |
| 348 | 348 |
"data": {
|
| 349 | 349 |
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAA3CAYAAAAooXA5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n", |
| 350 | 350 |
"text/plain": [ |
| 351 |
"<matplotlib.figure.Figure at 0x7f6bf7818748>"
|
|
| 351 |
"<matplotlib.figure.Figure at 0x7f1b00bd4cd0>"
|
|
| 352 | 352 |
] |
| 353 | 353 |
}, |
| 354 | 354 |
"metadata": {},
|
| ... | ... | |
| 361 | 361 |
}, |
| 362 | 362 |
{
|
| 363 | 363 |
"cell_type": "code", |
| 364 |
"execution_count": 25,
|
|
| 364 |
"execution_count": 16,
|
|
| 365 | 365 |
"metadata": {
|
| 366 | 366 |
"collapsed": false |
| 367 | 367 |
}, |
| ... | ... | |
| 369 | 369 |
{
|
| 370 | 370 |
"data": {
|
| 371 | 371 |
"text/plain": [ |
| 372 |
"<matplotlib.image.AxesImage at 0x7f6bde702a20>"
|
|
| 372 |
"<matplotlib.image.AxesImage at 0x7f1b00755cd0>"
|
|
| 373 | 373 |
] |
| 374 | 374 |
}, |
| 375 |
"execution_count": 25,
|
|
| 375 |
"execution_count": 16,
|
|
| 376 | 376 |
"metadata": {},
|
| 377 | 377 |
"output_type": "execute_result" |
| 378 | 378 |
}, |
| ... | ... | |
| 380 | 380 |
"data": {
|
| 381 | 381 |
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAA3CAYAAAAooXA5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6AAAAABJRU5ErkJggg==\n", |
| 382 | 382 |
"text/plain": [ |
| 383 |
"<matplotlib.figure.Figure at 0x7f6bde78fc88>"
|
|
| 383 |
"<matplotlib.figure.Figure at 0x7f1b007dddd0>"
|
|
| 384 | 384 |
] |
| 385 | 385 |
}, |
| 386 | 386 |
"metadata": {},
|
| ... | ... | |
| 393 | 393 |
}, |
| 394 | 394 |
{
|
| 395 | 395 |
"cell_type": "code", |
| 396 |
"execution_count": 26,
|
|
| 396 |
"execution_count": 17,
|
|
| 397 | 397 |
"metadata": {
|
| 398 | 398 |
"collapsed": true |
| 399 | 399 |
}, |
| ... | ... | |
| 404 | 404 |
}, |
| 405 | 405 |
{
|
| 406 | 406 |
"cell_type": "code", |
| 407 |
"execution_count": 27,
|
|
| 407 |
"execution_count": 18,
|
|
| 408 | 408 |
"metadata": {
|
| 409 | 409 |
"collapsed": true |
| 410 | 410 |
}, |
| ... | ... | |
| 415 | 415 |
}, |
| 416 | 416 |
{
|
| 417 | 417 |
"cell_type": "code", |
| 418 |
"execution_count": 28,
|
|
| 418 |
"execution_count": 19,
|
|
| 419 | 419 |
"metadata": {
|
| 420 | 420 |
"collapsed": true |
| 421 | 421 |
}, |
| ... | ... | |
| 426 | 426 |
}, |
| 427 | 427 |
{
|
| 428 | 428 |
"cell_type": "code", |
| 429 |
"execution_count": 18,
|
|
| 429 |
"execution_count": 20,
|
|
| 430 | 430 |
"metadata": {
|
| 431 | 431 |
"collapsed": true |
| 432 | 432 |
}, |
| ... | ... | |
| 437 | 437 |
}, |
| 438 | 438 |
{
|
| 439 | 439 |
"cell_type": "code", |
| 440 |
"execution_count": 19,
|
|
| 440 |
"execution_count": 21,
|
|
| 441 | 441 |
"metadata": {
|
| 442 | 442 |
"collapsed": true |
| 443 | 443 |
}, |
| ... | ... | |
| 458 | 458 |
], |
| 459 | 459 |
"metadata": {
|
| 460 | 460 |
"kernelspec": {
|
| 461 |
"display_name": "Python 3",
|
|
| 461 |
"display_name": "Python 2",
|
|
| 462 | 462 |
"language": "python", |
| 463 |
"name": "python3"
|
|
| 463 |
"name": "python2"
|
|
| 464 | 464 |
}, |
| 465 | 465 |
"language_info": {
|
| 466 | 466 |
"codemirror_mode": {
|
| 467 | 467 |
"name": "ipython", |
| 468 |
"version": 3
|
|
| 468 |
"version": 2
|
|
| 469 | 469 |
}, |
| 470 | 470 |
"file_extension": ".py", |
| 471 | 471 |
"mimetype": "text/x-python", |
| 472 | 472 |
"name": "python", |
| 473 | 473 |
"nbconvert_exporter": "python", |
| 474 |
"pygments_lexer": "ipython3",
|
|
| 475 |
"version": "3.4.3"
|
|
| 474 |
"pygments_lexer": "ipython2",
|
|
| 475 |
"version": "2.7.10"
|
|
| 476 | 476 |
} |
| 477 | 477 |
}, |
| 478 | 478 |
"nbformat": 4, |
| Vamp.ipynb | ||
|---|---|---|
| 22 | 22 |
"name": "stderr", |
| 23 | 23 |
"output_type": "stream", |
| 24 | 24 |
"text": [ |
| 25 |
"/usr/lib/python3.4/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
|
|
| 25 |
"/usr/lib/python2.7/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
|
|
| 26 | 26 |
" warnings.warn('Could not import scikits.samplerate. '\n"
|
| 27 | 27 |
] |
| 28 | 28 |
} |
| ... | ... | |
| 70 | 70 |
" 'bbc-vamp-plugins:bbc-spectral-contrast',\n", |
| 71 | 71 |
" 'bbc-vamp-plugins:bbc-spectral-flux',\n", |
| 72 | 72 |
" 'bbc-vamp-plugins:bbc-speechmusic-segmenter',\n", |
| 73 |
" 'cepstral-pitchtracker:cepstral-pitchtracker',\n", |
|
| 73 | 74 |
" 'chp:constrainedharmonicpeak',\n", |
| 74 | 75 |
" 'cqvamp:cqchromavamp',\n", |
| 75 | 76 |
" 'cqvamp:cqvamp',\n", |
| ... | ... | |
| 193 | 194 |
}, |
| 194 | 195 |
{
|
| 195 | 196 |
"cell_type": "code", |
| 196 |
"execution_count": 31,
|
|
| 197 |
"execution_count": 8,
|
|
| 197 | 198 |
"metadata": {
|
| 198 | 199 |
"collapsed": false |
| 199 | 200 |
}, |
| 200 | 201 |
"outputs": [], |
| 201 | 202 |
"source": [ |
| 202 |
"data, rate = librosa.load(\"data/Music/piano-scale.wav\")"
|
|
| 203 |
"data, rate = librosa.load(\"data/piano-scale.wav\")" |
|
| 203 | 204 |
] |
| 204 | 205 |
}, |
| 205 | 206 |
{
|
| 206 | 207 |
"cell_type": "code", |
| 207 |
"execution_count": 32,
|
|
| 208 |
"execution_count": 9,
|
|
| 208 | 209 |
"metadata": {
|
| 209 | 210 |
"collapsed": false |
| 210 | 211 |
}, |
| ... | ... | |
| 215 | 216 |
"22050" |
| 216 | 217 |
] |
| 217 | 218 |
}, |
| 218 |
"execution_count": 32,
|
|
| 219 |
"execution_count": 9,
|
|
| 219 | 220 |
"metadata": {},
|
| 220 | 221 |
"output_type": "execute_result" |
| 221 | 222 |
} |
| ... | ... | |
| 226 | 227 |
}, |
| 227 | 228 |
{
|
| 228 | 229 |
"cell_type": "code", |
| 229 |
"execution_count": 64,
|
|
| 230 |
"execution_count": 10,
|
|
| 230 | 231 |
"metadata": {
|
| 231 | 232 |
"collapsed": true |
| 232 | 233 |
}, |
| ... | ... | |
| 237 | 238 |
}, |
| 238 | 239 |
{
|
| 239 | 240 |
"cell_type": "code", |
| 240 |
"execution_count": 65,
|
|
| 241 |
"execution_count": 11,
|
|
| 241 | 242 |
"metadata": {
|
| 242 | 243 |
"collapsed": false |
| 243 | 244 |
}, |
| ... | ... | |
| 248 | 249 |
}, |
| 249 | 250 |
{
|
| 250 | 251 |
"cell_type": "code", |
| 251 |
"execution_count": 66,
|
|
| 252 |
"execution_count": 12,
|
|
| 252 | 253 |
"metadata": {
|
| 253 | 254 |
"collapsed": false |
| 254 | 255 |
}, |
| ... | ... | |
| 583 | 584 |
" 0.00000000e+00, 3.16866152e-02, 5.19289337e-02]], dtype=float32))}" |
| 584 | 585 |
] |
| 585 | 586 |
}, |
| 586 |
"execution_count": 66,
|
|
| 587 |
"execution_count": 12,
|
|
| 587 | 588 |
"metadata": {},
|
| 588 | 589 |
"output_type": "execute_result" |
| 589 | 590 |
} |
| ... | ... | |
| 594 | 595 |
}, |
| 595 | 596 |
{
|
| 596 | 597 |
"cell_type": "code", |
| 597 |
"execution_count": 67,
|
|
| 598 |
"execution_count": 13,
|
|
| 598 | 599 |
"metadata": {
|
| 599 | 600 |
"collapsed": false |
| 600 | 601 |
}, |
| ... | ... | |
| 605 | 606 |
}, |
| 606 | 607 |
{
|
| 607 | 608 |
"cell_type": "code", |
| 608 |
"execution_count": 68,
|
|
| 609 |
"execution_count": 14,
|
|
| 609 | 610 |
"metadata": {
|
| 610 | 611 |
"collapsed": false |
| 611 | 612 |
}, |
| ... | ... | |
| 613 | 614 |
{
|
| 614 | 615 |
"data": {
|
| 615 | 616 |
"text/plain": [ |
| 616 |
"<matplotlib.image.AxesImage at 0x7fe7fbadacc0>"
|
|
| 617 |
" 0.092879819"
|
|
| 617 | 618 |
] |
| 618 | 619 |
}, |
| 619 |
"execution_count": 68, |
|
| 620 |
"execution_count": 14, |
|
| 621 |
"metadata": {},
|
|
| 622 |
"output_type": "execute_result" |
|
| 623 |
} |
|
| 624 |
], |
|
| 625 |
"source": [ |
|
| 626 |
"step" |
|
| 627 |
] |
|
| 628 |
}, |
|
| 629 |
{
|
|
| 630 |
"cell_type": "code", |
|
| 631 |
"execution_count": 15, |
|
| 632 |
"metadata": {
|
|
| 633 |
"collapsed": false |
|
| 634 |
}, |
|
| 635 |
"outputs": [ |
|
| 636 |
{
|
|
| 637 |
"data": {
|
|
| 638 |
"text/plain": [ |
|
| 639 |
"2048.00000895" |
|
| 640 |
] |
|
| 641 |
}, |
|
| 642 |
"execution_count": 15, |
|
| 643 |
"metadata": {},
|
|
| 644 |
"output_type": "execute_result" |
|
| 645 |
} |
|
| 646 |
], |
|
| 647 |
"source": [ |
|
| 648 |
"float(step) * rate" |
|
| 649 |
] |
|
| 650 |
}, |
|
| 651 |
{
|
|
| 652 |
"cell_type": "code", |
|
| 653 |
"execution_count": 16, |
|
| 654 |
"metadata": {
|
|
| 655 |
"collapsed": false |
|
| 656 |
}, |
|
| 657 |
"outputs": [ |
|
| 658 |
{
|
|
| 659 |
"data": {
|
|
| 660 |
"text/plain": [ |
|
| 661 |
"2048" |
|
| 662 |
] |
|
| 663 |
}, |
|
| 664 |
"execution_count": 16, |
|
| 665 |
"metadata": {},
|
|
| 666 |
"output_type": "execute_result" |
|
| 667 |
} |
|
| 668 |
], |
|
| 669 |
"source": [ |
|
| 670 |
"vamp.vampyhost.RealTime.to_frame(step, rate)" |
|
| 671 |
] |
|
| 672 |
}, |
|
| 673 |
{
|
|
| 674 |
"cell_type": "code", |
|
| 675 |
"execution_count": 17, |
|
| 676 |
"metadata": {
|
|
| 677 |
"collapsed": false |
|
| 678 |
}, |
|
| 679 |
"outputs": [ |
|
| 680 |
{
|
|
| 681 |
"data": {
|
|
| 682 |
"text/plain": [ |
|
| 683 |
"<matplotlib.image.AxesImage at 0x7f1b8e6ca7d0>" |
|
| 684 |
] |
|
| 685 |
}, |
|
| 686 |
"execution_count": 17, |
|
| 620 | 687 |
"metadata": {},
|
| 621 | 688 |
"output_type": "execute_result" |
| 622 | 689 |
}, |
| ... | ... | |
| 624 | 691 |
"data": {
|
| 625 | 692 |
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAABOCAYAAADvu5czAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n", |
| 626 | 693 |
"text/plain": [ |
| 627 |
"<matplotlib.figure.Figure at 0x7fe7fbb4a438>"
|
|
| 694 |
"<matplotlib.figure.Figure at 0x7f1b8e99ee10>"
|
|
| 628 | 695 |
] |
| 629 | 696 |
}, |
| 630 | 697 |
"metadata": {},
|
| ... | ... | |
| 637 | 704 |
}, |
| 638 | 705 |
{
|
| 639 | 706 |
"cell_type": "code", |
| 640 |
"execution_count": 69,
|
|
| 707 |
"execution_count": 18,
|
|
| 641 | 708 |
"metadata": {
|
| 642 | 709 |
"collapsed": true |
| 643 | 710 |
}, |
| ... | ... | |
| 648 | 715 |
}, |
| 649 | 716 |
{
|
| 650 | 717 |
"cell_type": "code", |
| 651 |
"execution_count": 70,
|
|
| 718 |
"execution_count": 19,
|
|
| 652 | 719 |
"metadata": {
|
| 653 | 720 |
"collapsed": true |
| 654 | 721 |
}, |
| ... | ... | |
| 659 | 726 |
}, |
| 660 | 727 |
{
|
| 661 | 728 |
"cell_type": "code", |
| 662 |
"execution_count": 71,
|
|
| 729 |
"execution_count": 20,
|
|
| 663 | 730 |
"metadata": {
|
| 664 | 731 |
"collapsed": true |
| 665 | 732 |
}, |
| ... | ... | |
| 670 | 737 |
}, |
| 671 | 738 |
{
|
| 672 | 739 |
"cell_type": "code", |
| 673 |
"execution_count": 72,
|
|
| 740 |
"execution_count": 21,
|
|
| 674 | 741 |
"metadata": {
|
| 675 | 742 |
"collapsed": true |
| 676 | 743 |
}, |
| ... | ... | |
| 681 | 748 |
}, |
| 682 | 749 |
{
|
| 683 | 750 |
"cell_type": "code", |
| 684 |
"execution_count": 73,
|
|
| 751 |
"execution_count": 22,
|
|
| 685 | 752 |
"metadata": {
|
| 686 | 753 |
"collapsed": true |
| 687 | 754 |
}, |
| ... | ... | |
| 692 | 759 |
}, |
| 693 | 760 |
{
|
| 694 | 761 |
"cell_type": "code", |
| 695 |
"execution_count": 74,
|
|
| 762 |
"execution_count": 23,
|
|
| 696 | 763 |
"metadata": {
|
| 697 | 764 |
"collapsed": true |
| 698 | 765 |
}, |
| ... | ... | |
| 703 | 770 |
}, |
| 704 | 771 |
{
|
| 705 | 772 |
"cell_type": "code", |
| 706 |
"execution_count": 75,
|
|
| 773 |
"execution_count": 24,
|
|
| 707 | 774 |
"metadata": {
|
| 708 | 775 |
"collapsed": true |
| 709 | 776 |
}, |
| 710 | 777 |
"outputs": [], |
| 711 | 778 |
"source": [ |
| 712 | 779 |
"def extract_chroma(audiofile):\n", |
| 713 |
" data, rate = librosa.load(audiofile)\n", |
|
| 780 |
" data, rate = librosa.load(audiofile, sr = None)\n",
|
|
| 714 | 781 |
" out = vamp.collect(data, rate,\n", |
| 715 | 782 |
" plugin_key = \"nnls-chroma:nnls-chroma\",\n", |
| 716 | 783 |
" output = \"chroma\",\n", |
| 717 | 784 |
" process_timestamp_method = vamp.vampyhost.SHIFT_DATA)\n", |
| 718 | 785 |
" step, chroma = out[\"matrix\"]\n", |
| 786 |
" print(\"File \" + audiofile +\n", |
|
| 787 |
" \": sample rate = \" + str(rate) +\n", |
|
| 788 |
" \", chroma step = \" + str(vamp.vampyhost.RealTime.to_frame(step, rate)))\n", |
|
| 719 | 789 |
" out_file = open(os.path.splitext(audiofile)[0] + \"_chroma.csv\", \"w\")\n", |
| 720 | 790 |
" csv.writer(out_file).writerows(chroma)\n", |
| 721 | 791 |
" out_file.close()" |
| ... | ... | |
| 723 | 793 |
}, |
| 724 | 794 |
{
|
| 725 | 795 |
"cell_type": "code", |
| 726 |
"execution_count": 76,
|
|
| 796 |
"execution_count": 25,
|
|
| 727 | 797 |
"metadata": {
|
| 728 | 798 |
"collapsed": false |
| 729 | 799 |
}, |
| 730 |
"outputs": [], |
|
| 800 |
"outputs": [ |
|
| 801 |
{
|
|
| 802 |
"name": "stdout", |
|
| 803 |
"output_type": "stream", |
|
| 804 |
"text": [ |
|
| 805 |
"File data/Music/07 - King Henry.flac: sample rate = 44100, chroma step = 2048\n" |
|
| 806 |
] |
|
| 807 |
} |
|
| 808 |
], |
|
| 731 | 809 |
"source": [ |
| 732 |
"for file in glob.glob(\"data/Music/*.wav\"):\n",
|
|
| 810 |
"for file in glob.glob(\"data/Music/*.flac\"):\n",
|
|
| 733 | 811 |
" extract_chroma(file)" |
| 734 | 812 |
] |
| 735 | 813 |
}, |
| ... | ... | |
| 745 | 823 |
], |
| 746 | 824 |
"metadata": {
|
| 747 | 825 |
"kernelspec": {
|
| 748 |
"display_name": "Python 3",
|
|
| 826 |
"display_name": "Python 2",
|
|
| 749 | 827 |
"language": "python", |
| 750 |
"name": "python3"
|
|
| 828 |
"name": "python2"
|
|
| 751 | 829 |
}, |
| 752 | 830 |
"language_info": {
|
| 753 | 831 |
"codemirror_mode": {
|
| 754 | 832 |
"name": "ipython", |
| 755 |
"version": 3
|
|
| 833 |
"version": 2
|
|
| 756 | 834 |
}, |
| 757 | 835 |
"file_extension": ".py", |
| 758 | 836 |
"mimetype": "text/x-python", |
| 759 | 837 |
"name": "python", |
| 760 | 838 |
"nbconvert_exporter": "python", |
| 761 |
"pygments_lexer": "ipython3",
|
|
| 762 |
"version": "3.4.3"
|
|
| 839 |
"pygments_lexer": "ipython2",
|
|
| 840 |
"version": "2.7.10"
|
|
| 763 | 841 |
} |
| 764 | 842 |
}, |
| 765 | 843 |
"nbformat": 4, |
| Vamp.v3.ipynb | ||
|---|---|---|
| 1 | 1 |
{
|
| 2 | 2 |
"metadata": {
|
| 3 | 3 |
"kernelspec": {
|
| 4 |
"display_name": "Python 3",
|
|
| 4 |
"display_name": "Python 2",
|
|
| 5 | 5 |
"language": "python", |
| 6 |
"name": "python3"
|
|
| 6 |
"name": "python2"
|
|
| 7 | 7 |
}, |
| 8 | 8 |
"language_info": {
|
| 9 | 9 |
"codemirror_mode": {
|
| 10 | 10 |
"name": "ipython", |
| 11 |
"version": 3
|
|
| 11 |
"version": 2
|
|
| 12 | 12 |
}, |
| 13 | 13 |
"file_extension": ".py", |
| 14 | 14 |
"mimetype": "text/x-python", |
| 15 | 15 |
"name": "python", |
| 16 | 16 |
"nbconvert_exporter": "python", |
| 17 |
"pygments_lexer": "ipython3",
|
|
| 18 |
"version": "3.4.3"
|
|
| 17 |
"pygments_lexer": "ipython2",
|
|
| 18 |
"version": "2.7.10"
|
|
| 19 | 19 |
}, |
| 20 | 20 |
"name": "" |
| 21 | 21 |
}, |
| ... | ... | |
| 48 | 48 |
"output_type": "stream", |
| 49 | 49 |
"stream": "stderr", |
| 50 | 50 |
"text": [ |
| 51 |
"/usr/lib/python3.4/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
|
|
| 51 |
"/usr/lib/python2.7/site-packages/librosa/core/audio.py:33: UserWarning: Could not import scikits.samplerate. Falling back to scipy.signal\n",
|
|
| 52 | 52 |
" warnings.warn('Could not import scikits.samplerate. '\n"
|
| 53 | 53 |
] |
| 54 | 54 |
} |
| ... | ... | |
| 98 | 98 |
" 'bbc-vamp-plugins:bbc-spectral-contrast',\n", |
| 99 | 99 |
" 'bbc-vamp-plugins:bbc-spectral-flux',\n", |
| 100 | 100 |
" 'bbc-vamp-plugins:bbc-speechmusic-segmenter',\n", |
| 101 |
" 'cepstral-pitchtracker:cepstral-pitchtracker',\n", |
|
| 101 | 102 |
" 'chp:constrainedharmonicpeak',\n", |
| 102 | 103 |
" 'cqvamp:cqchromavamp',\n", |
| 103 | 104 |
" 'cqvamp:cqvamp',\n", |
| ... | ... | |
| 217 | 218 |
"cell_type": "code", |
| 218 | 219 |
"collapsed": false, |
| 219 | 220 |
"input": [ |
| 220 |
"data, rate = librosa.load(\"data/Music/piano-scale.wav\")"
|
|
| 221 |
"data, rate = librosa.load(\"data/piano-scale.wav\")" |
|
| 221 | 222 |
], |
| 222 | 223 |
"language": "python", |
| 223 | 224 |
"metadata": {},
|
| 224 | 225 |
"outputs": [], |
| 225 |
"prompt_number": 31
|
|
| 226 |
"prompt_number": 8
|
|
| 226 | 227 |
}, |
| 227 | 228 |
{
|
| 228 | 229 |
"cell_type": "code", |
| ... | ... | |
| 236 | 237 |
{
|
| 237 | 238 |
"metadata": {},
|
| 238 | 239 |
"output_type": "pyout", |
| 239 |
"prompt_number": 32,
|
|
| 240 |
"prompt_number": 9,
|
|
| 240 | 241 |
"text": [ |
| 241 | 242 |
"22050" |
| 242 | 243 |
] |
| 243 | 244 |
} |
| 244 | 245 |
], |
| 245 |
"prompt_number": 32
|
|
| 246 |
"prompt_number": 9
|
|
| 246 | 247 |
}, |
| 247 | 248 |
{
|
| 248 | 249 |
"cell_type": "code", |
| ... | ... | |
| 253 | 254 |
"language": "python", |
| 254 | 255 |
"metadata": {},
|
| 255 | 256 |
"outputs": [], |
| 256 |
"prompt_number": 64
|
|
| 257 |
"prompt_number": 10
|
|
| 257 | 258 |
}, |
| 258 | 259 |
{
|
| 259 | 260 |
"cell_type": "code", |
| ... | ... | |
| 264 | 265 |
"language": "python", |
| 265 | 266 |
"metadata": {},
|
| 266 | 267 |
"outputs": [], |
| 267 |
"prompt_number": 65
|
|
| 268 |
"prompt_number": 11
|
|
| 268 | 269 |
}, |
| 269 | 270 |
{
|
| 270 | 271 |
"cell_type": "code", |
| ... | ... | |
| 278 | 279 |
{
|
| 279 | 280 |
"metadata": {},
|
| 280 | 281 |
"output_type": "pyout", |
| 281 |
"prompt_number": 66,
|
|
| 282 |
"prompt_number": 12,
|
|
| 282 | 283 |
"text": [ |
| 283 | 284 |
"{'matrix': ( 0.092879819,\n",
|
| 284 | 285 |
" array([[ 0.00000000e+00, 3.95574346e-02, 1.29732716e-05,\n", |
| ... | ... | |
| 608 | 609 |
] |
| 609 | 610 |
} |
| 610 | 611 |
], |
| 611 |
"prompt_number": 66
|
|
| 612 |
"prompt_number": 12
|
|
| 612 | 613 |
}, |
| 613 | 614 |
{
|
| 614 | 615 |
"cell_type": "code", |
| ... | ... | |
| 619 | 620 |
"language": "python", |
| 620 | 621 |
"metadata": {},
|
| 621 | 622 |
"outputs": [], |
| 622 |
"prompt_number": 67 |
|
| 623 |
"prompt_number": 13 |
|
| 624 |
}, |
|
| 625 |
{
|
|
| 626 |
"cell_type": "code", |
|
| 627 |
"collapsed": false, |
|
| 628 |
"input": [ |
|
| 629 |
"step" |
|
| 630 |
], |
|
| 631 |
"language": "python", |
|
| 632 |
"metadata": {},
|
|
| 633 |
"outputs": [ |
|
| 634 |
{
|
|
| 635 |
"metadata": {},
|
|
| 636 |
"output_type": "pyout", |
|
| 637 |
"prompt_number": 14, |
|
| 638 |
"text": [ |
|
| 639 |
" 0.092879819" |
|
| 640 |
] |
|
| 641 |
} |
|
| 642 |
], |
|
| 643 |
"prompt_number": 14 |
|
| 644 |
}, |
|
| 645 |
{
|
|
| 646 |
"cell_type": "code", |
|
| 647 |
"collapsed": false, |
|
| 648 |
"input": [ |
|
| 649 |
"float(step) * rate" |
|
| 650 |
], |
|
| 651 |
"language": "python", |
|
| 652 |
"metadata": {},
|
|
| 653 |
"outputs": [ |
|
| 654 |
{
|
|
| 655 |
"metadata": {},
|
|
| 656 |
"output_type": "pyout", |
|
| 657 |
"prompt_number": 15, |
|
| 658 |
"text": [ |
|
| 659 |
"2048.00000895" |
|
| 660 |
] |
|
| 661 |
} |
|
| 662 |
], |
|
| 663 |
"prompt_number": 15 |
|
| 664 |
}, |
|
| 665 |
{
|
|
| 666 |
"cell_type": "code", |
|
| 667 |
"collapsed": false, |
|
| 668 |
"input": [ |
|
| 669 |
"vamp.vampyhost.RealTime.to_frame(step, rate)" |
|
| 670 |
], |
|
| 671 |
"language": "python", |
|
| 672 |
"metadata": {},
|
|
| 673 |
"outputs": [ |
|
| 674 |
{
|
|
| 675 |
"metadata": {},
|
|
| 676 |
"output_type": "pyout", |
|
| 677 |
"prompt_number": 16, |
|
| 678 |
"text": [ |
|
| 679 |
"2048" |
|
| 680 |
] |
|
| 681 |
} |
|
| 682 |
], |
|
| 683 |
"prompt_number": 16 |
|
| 623 | 684 |
}, |
| 624 | 685 |
{
|
| 625 | 686 |
"cell_type": "code", |
| ... | ... | |
| 633 | 694 |
{
|
| 634 | 695 |
"metadata": {},
|
| 635 | 696 |
"output_type": "pyout", |
| 636 |
"prompt_number": 68,
|
|
| 697 |
"prompt_number": 17,
|
|
| 637 | 698 |
"text": [ |
| 638 |
"<matplotlib.image.AxesImage at 0x7fe7fbadacc0>"
|
|
| 699 |
"<matplotlib.image.AxesImage at 0x7f1b8e6ca7d0>"
|
|
| 639 | 700 |
] |
| 640 | 701 |
}, |
| 641 | 702 |
{
|
| ... | ... | |
| 643 | 704 |
"output_type": "display_data", |
| 644 | 705 |
"png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAABOCAYAAADvu5czAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmsbct63/WrGs3sV7/P7s7ZZ5/jc+717XJzEyM5kUwM\nMhIKUZ4QAoGIojzwkigKAhHlwQIkRCOhGPGAhARWLCIUMFEUISTkSBjyAjYxhMixud1pdrf2Xt3s\n52ireKj65qgx1ly7O74mV9rfVu2aa87R1KjmX9/3/76qoay1vJN38k7eyTv56RL9/3cB3sk7eSfv\n5J28ubwD73fyTt7JO/kplHfg/U7eyTt5Jz+F8g6838k7eSfv5KdQ3oH3O3kn7+Sd/BTKO/B+J+/k\nnbyTn0KJf1IXVkq9i0F8J+/knbyTtxBrrXrVMS8Fb6XUfw38C8ALa+13/HdHwN8CPgQ+B/4la+10\n9xX+PeB/Af4ZKdLrlZwoSBroPocNknmN68XAQZD2/Xdyj/8C+IvBNUVUcH/TSQWQARuf50AZpCJI\n8vebiY4gSi1RYokSKNe/yTD9BXRp0GWNqi3G+hLZdum7NS1/q26KI0hiSGJUmlCrmMomPqUYq8EY\nqI3LjbleFZRA5dNvAL+4485dUZ3Pu5L8loJ+D/QtUO+BPgQ7BTN1uZ0BEdgYiH0+AEbA2Ocxri01\n8NeBfwN4HqRF8Luk2ifj8265dz1H+Dw2ON+8pC5ukt+kXZc/AVEQJaAT0ClEiUKXI6JyRFSOicoR\n1q6oWWDsDMMci8XVZ0zO/0afX9pxWeX/V6BApyUqKVFJBXFFWSVUVUJZJZRlAtYESer6pt4MxPvQ\nf79J0TFUCkoNlYZaNUN8/e/D4S9DaaGwfoj6gUMwcG4B9y28D9wHsit4/BgeP3H5bAWkPvVo4HNX\nu3b7gmCJnCsJmr7xN4B/DfjnX9pk2yp4xe+/CvznwK8F3/1V4Destf+JUurf8X//1d2n93C1l9AG\nXJEuUIoImyMPJa3gB+cWUHcNdPlsg/OVL4sF1jiQiYOUAdMd5QkrXQagDMbSnyfAXdAAWJi2CPfG\nkgws45Oa0YlhdGyY/qjkZ45n9C8z+pcZ0aIkt5AZyI3rlyGm7qpteWKpTT1KUMcp6sSlTTphXh0w\nrfaY1QdkxQA2Bjb+RpltHjnD3ZS1Txua9n4ZYHXbTibRsE10873uQXoI6T6kI0j6UI39YE2gGoCJ\nwGiX2wjod1JE0ze0L2MfB+x7tPuNpIg2gOwakN1+EgV5RTO5Z/7vf7JEaegdKgbH0D9x+ehyzfBi\nw+jynNElFNWGzK7I7JrMrKi3dRNxyhV3+fG1mtEotIfwKIb4yBCfGOLjGnUAl9NjrqZHXE4PuJoe\nYascqsylOvMAHo6dTj+yFdQrKC9BRaDXUPegTqFKwSQeAizUBWQLp4BUBuraKSHWuoT/XAALC2ce\n0Ms5XE5htYYqHMvhZC5luwk7pF9omsm89MeZzjXC314tLwVva+3fV0o97Hz9Z4E/5T//DZx6cAN4\np7TBO4SUlw2K8DdDM6B7uAEXwk+onYdaelc9lM6wwmlZSXDdDXAVlHtXxYfXkUoW0JY8vE+otb0d\ng5QMLZM7hpOPK44/rog2Fd/6aMbeFwv2qzlJlrGo3dMsLaysu2MV3F1qUfJw3k+BaJii7/XRH/VR\nP9NnPjQ8zSfoos+mOCFb78PMNmlu3Q0X/gaFAWY+SV3Fwd1vmrhU5/hQG0naSfUg3YPhHoxGMOhD\npiBPIOtDPvbalr+e1WDDa6Q0/cpyHbwL/1130g37VFdhCL8Pn0NSggPsuT9OrJN/skRp6B3A5EPF\n/scuHX++4ujzFUdqxfFixcZmzE3ukioordShJueKjz14hzXioF25URorekfQewjpxxDdj/jiSZ8v\nH59QPT7gqnrgwLWYA3MHspQ0fai+Vu4GvCOwJagFmFGTbN8PQw/e+RLqCkzl89Jp+VifG8itA2/5\nXK1gMQvAW/CoO7pCXOv2kyisbdrjogq+l+d8fQv9bTjv29ba5/7zc+D2zYemwM/6vGs+hqk7U3WB\nV2b6HjCkgZ6UBoTDgQRt8CzxEIcD7zVtgPgIp3lDWzsLkw2uKeAttEiOa4gunfMm1M6O2htY9m7X\n3Pq04v3vldweHvNtNeOkuuDW1QW9qyWXCi6BC+PgMyQwZA4PSzXwaejzeNQjujtC/+yQ6HtDzvZS\n9KZis+lztjmG2XtOEznD5SkNo7CRehZwrICv+/aQ5w4nY5GwrWRy7wcl69MCc9WHZAijIewPYTyA\nVQLrPqjaDUjpO6Y76crkDs3A+6dpg3ftj8uDVHLd4guvG1oM4XPI9Jji+poA9+oVrb1LHr7FOW8m\nSkP/ULH3UHHyHcWtP6q5v7fmHmfcW7zg3tMXLKqCcyrObc05NUUwZhNyHvDja6MlVI3SWDE80gw/\nihh8VxN/vUf/929R9yKuqgOYPgB16c6qLag86DIy7joi4G1LqJagZmAPwO57rd2foyzYP+rA2xRg\ng0TtgdvjRGFgbpyFObdgMsjXUNykeYfjW8Z72D/CeJAujSbAHYL91/kD07xfJdZa+3LHZAJ8E1fg\ncNbZBd7hA4fHySBJcYN77HMZ4KKJh10HGpCtaMB1hdOGZjTdKwHu4cB7l8Z9E3hXtPnsHR3sK0rc\nswyPLYcPau58o+LBd46491vPuftixt3ROYN4xgsLL2rYU3DRKVHJ9ZpuWGDFGEgGfaKTDdGHI6Jv\n5gwOD5itDafLlGS1D5cnDk8TXyh57LWvvq2eL19+h7b2sMvyCCiRrdYtbTumAXAP6KoHSQr9FEYp\n7MWgYndZKY+MoRvjp8I+9Qu4fiAALv0z7Dti8QkgJ1zvE6HfRJSLcOJJ/X2WtDWw15WHN3z/Sl/W\na4vSkEwUozuKg080t76rubfJ+eh8ysMvn/Iw/oJpVHJqYaKgjyIPzr+PvTZVRqig1hS9SDHejxnf\nSxh/GpP8kSGLuuDFKmZ4to8a3MXWEVQ15FJfoUXepSVwNEftKZatTSnjsHvetxzAt/g+wYRgTBce\nwLdgLCpQSaOIdOu+q7CJCGZsa5oGwE2Qh3jzCT9pzfu5UuqOtfZUKXUXeHHzoX+H5sE+9mkXdRIO\ngi4Qa9xgHtFAj5jWon2H1ElIm9Q0g25Eo1F1wWSXM6o7oYQmU5cW+clIudEsThPOvq9I+gl5BJe/\nV/HsMRzPY3rlAdMKLg1cWWegS3cT7RsU1idQ9NAMUAzQDNDEqwT9tEf0+yk67XE+2eNxprjKVhSb\n57Co4ZwmXeDmvrW/EQY32CoccJ3QdtxKx++alqFG26NxLI5oJmTfPW0JRQmrpTutpk2zi7vhpc3R\nbbvQZ9Ed1NJP5FjxkXS1qi7HHdI0qS/g3BfybSf3l/l2uhTOLtoxzOVzk6yx5FcR8y8i0j2NJcL8\n7j6bz25xeVHxrIhY1jUXRnFhFJdoDy/NNV0JQhBXxCQkxMTEJFXM4EIz+CxiMNbEqx7/7w8e8vRH\nxyyeJ9hV5vwqRQz1GDhiS0OqkKIIb1vRbjdRDnNcve+iX8MggpzdFr6cE9oPfZp+m9LGnbBPhWAs\ntdGl0mquK4H/D/C77FZ0bpa3Ae+/C/w54D/2+d+5+dBfoj3ThNQCnYJKZXVZWdFmBkEuFdd1YEIb\nvENTZkybR+sCTLdM3QHSNZHCBns7TvtVUmw08+eauJdQ5ZZZFLP3CCZPEibzAUm5ZmlgaRzfvabd\nLQwK64eT5AkRKTEpESkR0Uqjnml0qtGZYj7Y43mhuSpXFMVz2Kwd/ixo8iWN37cl0j4yfUjejdro\ndmqhw0Y+T/31fJ3bEooc1t63UBRthqPgNebSUNsJAfymFJq3oljcBJohkIfPlfsK+4MA7+7EcV3f\nbbT7rr1lO983fdgaQz6NmX8Rg43IZzGbR3tcPap4dhHxeTlgU1sWRrO0EQu0b/bmmq6ETa6JiOij\nGRDRJ65S0ktIP1OkBqIXMY+fPuDp6RGL0wSWmdd6E6hH/tLm+pAWZdxC40+Y07STWNkrroOxZfeY\nD4/r3jD0q8nkHPpmxLcTYkFXOv6bljZf4vrGd4FPaQbV/7jjOtflVaGC/y3OOXmilHoE/DLwHwH/\nnVLqL+BDBW++ghhYuzrR9i401EjIQ3Y17e6MF2rmu0yZ8L4RDZBII4na1nUsdk20LnjLbyEY/GTA\nu1xrFqcRVR6xPIvoqz69WUJvNqQ3nxCVObl1AR8SAdUulcK2BnaEJiEmISImIkGvLOpZDZsa9bwm\nTxIWtWZZrynq51BeXVdKwwhIFDChoTwm7I66CVMXvEO+e+C/Czq4KaFcwXrhIgBWq2ZOCDXu7nht\nyS5AM52TuwNxl7OSGz7votpC7fCrgHeo4Xc/x50U+oxe9swu2dqQXTmtsJjFLB4lXM32GUwjBrMB\ng2KfqlbkNiK3MbmNPES1x4PYd+5zjGaMYoxmjK76RJeWyFqiqUX/GGaLQ2bzA+aLBLvcOCdlHTvw\npgfKY4LSoCKwquM+kbA9SxPJI5q3+BmgjQXSYeTYbkfpToyCRwLWAZVHn92adLe+Q9xKaAIdZCBJ\nP7E0A+v15FXRJv/KDT9dD+zcKbsKsov/hPbMNqYdly3mi8yEXXpjl3YRisyQAtzS+Q2u4UMAl/S6\n8pMBbnC0ybxIWJylaJ06iDADMBOUKVC2ck9rd0+L7lm7g9tZM0o61LpCZRk8z0BnWFVhrMGywrDY\nPf5bN4uAuzQd+4Q2qnY1jZLdmvcgSAo3sXptyZZQ+LAwzkFd3cwI3Ci7fuzSES+7yOvwzLuUiLfp\nU13ZpWGH2mDIy9/k8A/BuyHWrKnJr1KKeYJ6lKB0ijIaZQYoc4A2JdZqLDGWBNuKbb4ppahwTUU9\nQl3WMK1RugZtMCZyyUbOMWj9s1gfXqxiUAmo1OXoTtPMaMav8HgC3rX/vivdibo7CUedvwW8Q0pP\nFAxxBIX0RxdDDNdZhJwmfFSUx7W/Z+G/ez15a4elUuqvAH8BV6X/CPjz1tq8fdR7cjTtigpHXhdg\nxAO1oaFEupx4V8JBEqJLSKFsaCpNSFIZUDo4fpemvWtAh5xYQpxYBuOM4SRjON7Q7+dEa0O0Nuh1\nTbQxVLVXMGqXdg2xsHYiFZGqlEQnpDrB9mKyQUw2jNgMYoqkT5nHVHlCXSRUedzGygqnsRiFC5/r\nmt/gVvl0ny/U0LpWyC4RnnFOs/DgJkAMve6WRhOSNhfPaMhHb8AuwL4sVnoXN/wy2aVFd1vkD0K6\ndd41zUPZpYC0Ne3eyDCY1PTHlv7EEucKtQS9VKiFgtJiMFgkFxtMrusoBuUBR2GIVEKsYpd0QjaO\n2QxjsmFCNkgosz7Vqkm2wkX42BpsxTbUbnt9URL8uLIG6shp1ts6CI8Xmiqsq8RpJS0JlSzxs8Q4\ncA3rW/KuQ7nbvt0+050YBZNEu5eyCvCKUrhL+ZPnCyfWUPMWi2yOGzvC/b2+MvhW4K2Uug/8JeAb\n1tpcKfW3gH8ZF/cdyH12c4RdTSA0qYUHgobD6jZCy35iNwfdBeJiRxJTVq6p2d0Au6gRWQjiZuE4\nsRycXHLr/ppb91ccHsxIz8ptSs5LcqFrC8jr687Fro7U0xHjJGKcxox7MWbS5/LWHlcnE65u7TEb\n9FjPR2xmQzbzIdVi4PqBREOuAeNnDMlbWoI45oqgJKHp1wWaXYCj/PlLGhMw5Pik84dtI20e1mto\nrsogkXKJtiK6gWiYYRl2ccKvKwIm3Zb4qhKCQhfAw7EAu+kN2zlWMZhUHN0vOLpfcnS/oLcoiR6X\nRE9LorzCljU1lhpDhcUGtIZcW/mxojBoZejHEf00op+4/OrWvutnt/a4vLXH+iphczpkfTqhPh1T\nbyzUhU8StRGOGVGGKnwnpB1CKSAmoC0TclAX1sf3k3pt3ODC+6RPCPhpHFUn4cghfdEJOb0GsK/y\nH4R+EdHkw769y2HZbb+u5SuKShi2JZEwb6YwfJVQwRgYKqVqnA3x5PohAt5dLjAExhpXeEmiIYfO\nhy54i+zm8a7zldCuYAltE/COaXc807nmLpHQsAmwT5xY9k/W3PvY8tE3Vty/e8Hgs4zB5xkDndPL\nctYbj60G1qVbByB+79D3LU800orjVHM00BwPNdXxhCcP7vD0Q82ThxPivT7Jiwm8OKB8cQDnExfx\nuA27tlD5Dr/t+KI1hIuMwoEUPu9NPC+dY0oaTWhNmwIZdtpp12TdHQC7PPJh2cSh2TV5d/WR1xEp\nm7RCVzl4Wwn56rAPh0DeBe+uItKepAaTmqP7Gfe/seb9b64Zna9JeiuSYk18tsKuKkqsT1AHWnfj\nWHT3cR4RyzjSTFLFZKAZDzRP7tzhyUd3ePowJvloj6snKfoHQ2q7R7Y4hNpCuQHWuBWt3QVq8kzi\nhaloR4kNcX0wpHC6fcADvO3R9JUNWMEH0dQjGlojdMgILIV0R7dub4oekjYSSibDOZ6L4DdR+G6i\np0KlJLSeus770PP+hwDe1tonSqn/FPgSV5P/s7X2710/8h7XZ7PuA5e4GDShNmQwZzQV2zVl4OXa\ne9gwkqANQCEYhJq3XDNcUbVLNG5mnwBHxKnl4OSc+x8bvva9FZ98dM54smYSrRlvVgwvN8yVWwMw\nL5sAsjCF+osF9jXcTRR3Boq7YyiPD/nhg4jhN8fwLUt11IdHE4ovj1lPbkHvsAHurQGzdh3erEFt\nPHUig006TejI6Wrcr0NHSJutaTShPX+PkEeUDi2T5y4vqGgl3UHQ5Xm7ZdrluHuZdEFTal+0wD8I\n2iR0KobaWggQ3X7cHdzteuhPKo7ez3n/m0s+/fkZ+0+m9PIp6fmU3o+mGPIWHAjENeBNAOIQKziM\n4bCnOBzC4Vjxw7sle58kJN/ao/w28MMEY4dki33002MX1meXDrjFsbiT9xUFQWgU0UQHNFqt1H3I\n9ZWurmz4nVh3kqDNRcc05qb25w1xffHA57tok64DOARbAe5QwezKy6zzkAGANutAcN7bUXVvS5sc\n4pbJP8Q94X+vlPpXrbV/s3VgOmELvvYm8PYats39DJ7TeOCs571CnjSsrHAm3QXerlGU1uhBRDTQ\nRIOIqKdIi4y0yEmLjF6RY2qDqa1LxmKsGJ5tA7Sp9k2LRzzE8J4+53Z8yd1kyt3BgnS/InnPkryf\nYvOIehpRziLyacS6H5MVsKkgKyGroDYGQ71NKZa1tawMLGuoqj7rIiHbRBRLqFJDvaoxmxKbeU6m\npchYV6dh3UYG4giiPsQG6DlKxXpqxVoP8Mrl9iXgvaWKjTvPGM9/ivUi0Ra6fd6NG3iFe8TsmkAE\nuLvd1nKdU36VdAfUrrUCbyhxBGniNvpKE1JlGZiavikY2DVxDXUVYaqIuo4wtfL9ymK2PaxrfbS1\nudssuKvm3FNz3ldz+skGMzTUez3y40PKsqIoIS8teWmpa0eTKE+T4DVvicuOsPQspEaRGIhrWBUJ\nm0xTrKFcGKpVRZ3n2DLDmg2O697gfBDiQ+paS+EzCMV402S9K1RTgCycCMJN4ISiC7lraHAipDxy\n2lt0hFb2LmZAUugnE4tV7rHLP/G61toukH9zeVva5JeAz6y1FwBKqb8N/EmgDd7qP2RbsfEvQvzP\nNn9vnRkRbh+CPV8PCc3udR5UrnWMkNjfNXuFmoxCxZb0yNK7o+jfVvSPFQfTjIPplIPpFQfTKVVm\nKHMoc0uZWUoMJbVPhhrbmjYsPRSXwCmKPfaN5e7mKbemzzh4ccFokpGVfdbjMfmDPptRj6uLAVcX\n/W2eLxTFkm2qbYElw9gMS87Q1FwUlicbyxGW6mLIsy9OODUjni3hYrxhcTFjcwbVeQkXC6fSbxfR\nWKhyMHkD4gkw1DAYu71CrIXKuNVtlfHjTvmqVC65xvRAznUsr2q23thKBqsAdk4bDBVtugSaiVbM\nBtGsw8HVpR662orkMnhfB4C7fpE3HYQd6aWwN4b9MeyNGEQZt8sLblcz7lQXDPIN+VpTSKoVNVbi\nPqhv7MtSJsuH2Yb7VyvuPFtz67M15SLlIt/ncjjm4v6YVRJRLQzl3FAuDKZ2da08SCrvpBQgj6xh\nVCnGBYzXipGBZ6e3eJYeclqkvLgqmT1bsfosIj812NXGxaVWG1yUSEZ7IVa37GJ9CWUS+e9DUAw1\n7NAiVsE1Qssw9HuJghBxPSgBGtCXePA2NuwOB5XPa9zmE0uaZbzdvvE2k33XqvwMF3X9ZvK24P0F\n8PNKqQGupn4J+K1rRx39u758ChftID/Iw/oBXg98e/sg/do3og0dayE7HNIZN/FNTSOpWJEew/Aj\nGH+qmDyA+88y7j694t7Tp9yPn5EtLNnSuo3zcktmLRnGJ8chto3ZBEUf5WM+92vL3c2UW9MZh8+n\nDEcZKztiOt7nfHTC+fvHXD7f4/LFhIvne1xO9ijPobrwmLcBYzZgl1i1wNolaV0yKg1DLKPaYoiZ\n2QHT5YDZc1j0N2yWkC1KquUKlv02D1NYMJVPfnCkIxhNHMAcTFy75NYtkshtEAarvGKjAvzogLfG\n1XFRg6p8e3VNYGmvsJPLYDTBbyF1JX1jlykrA0usrZBu6GphL5PutbsD6i2kl7o6vX0Mt48YxDPu\nFDM+zZd8rXjM3vKS9VSzRrEuFJtcUWC9/ml9ZHJogncnE8sHecH704K7zwpu7edc1rfIin1Ohw/4\n4b0PmfWG1Gc1ta2pswqTF6ggNE15MkX5+tIYejX0ckXPKHolzE5HzMoR06uU2eOK9XRFdl5TvMgw\n67lTCmoff7/lu28ag9C2mKTtwo0cRHPftUw27CtCaYVtJn0M2p1fnPEbmo3odtXpTdSg0Ivi/Q+t\nATrHvYpWDKU7gYBbef5RUKb/9TWu8/ac928ppX4d+B1c7f0O8F9eO3DgHUfWA4ANH9jnVkHdh8oD\nt65A+YFvpYMIlyUNH87OXW3legdSsSU5gtFD2P+u4uib8MEPMz6ZXPFJ/JRPih+z1paVsawKWCnZ\nwsr6BLkH72b+1ygiFDGKiImBu+uCW9OcgxcFw5HGHMbMDvZ5fHifzycfcvH0iPPDYy7Gx5wPjjGJ\nckzDGswUqBZYrsBeAVdokxOXhrg2JIXBZoZiaSmeG4qepYg21GVBXa6oyxiqqLN4xdNOsk8yFhIN\noz04nMB7t8GkXlmx7T4f+jC3E3DQfFtXgnXtZUrc5j0lzbaD4kUPwbtLm4XgHfLZoWMoCm4sfUf4\n0pBaEHkdAA5DUKVMX1F6iQPvuyfw4T0GacLtzRd8bbPk5zaPOZk+dWsCC8V8CUsUGbZFBuzW/pvP\n9zLD/WnNnVPDrbRm3Tsks/s8G37M7w++x9ngEGsrR6VdSZSE8LWy2rNCSUexNVGliIwiKhWRVhQl\nFFdQ9BR5v6IuVtSbjCqbY7PI9ysT0GQ3gaLk4UrokNbYlbrau1Bo4Zjuat6CAbLwTjryht2gGtZt\n1yoMP++iY0NLrUu7vK61F1pUb9/vvkq0ya8A3wa+BfxTwB8H/vfWEdHm+iTcGleq+U3jtEDTA9UN\nqyq57vgKO8FNwO0qRimFTmr0QBNPDOkhDPZKRqOcvf6Gw2RJGltHBavrzKcYfmEztuFI0atBbaC6\ngs2pYpb0ODcjTnv7PDo85seD21wOTrgc3OJycMLF4ATb040lCTi+I9hVz2aOygh5v2UY/ywIe5Ps\nADE1gaiGREHaA9tzWnYNlOr6+AmrWy7XCuTx4K1LP+kmuIEj5nFFe0B329CGFws+74owCjtRGF52\nnRt+NYBHO46TB+6Wpys3XFvHECfQ68FwQJT26KEZUrNvNxz2lm69SdToMd11k68CgCQHPYPquXUx\nEJMel4M9ng/e4/HgAc8HJ5CWEJXOGtpqnrK3wYp2T67Z7oUupcnCPhauXrwBoJT/T4FzYOKOVdLe\nfuxa3HWsTLzBxL1dgyCjzXaques3gaYfiJUXToO79i65SUImIKgH7cEgilzSyj1TK+kmofwju4Ir\nJbsK+b+xKGo0taevwns3z7R+yW5RoXwV8P7PgP/JWvsvKqVinMu3LesvpTxNLlpcODPXylu+ypnp\nVe35bqFOMhpPcvetNeGsuIvTspjKUFxa1p8bokEFCxg8UpjHQ1aPTzi7yMnnls0aNqV770BogMmO\nA13GXW2rX9GvFfOV4uxS8SRRDKoej+e3eXy2x6PHCReHJYvzNeuLGeU5cFHCC+W2EZdQWLuiGWSy\n41JoRnephl0dOqzwHQOtLN3y8qtLIHbgnSuftLdgVZs+gcZ6Atc5tfWd1DjKpfRthgFvqzROoV10\nxkvKeOMzhW3bXbn5JsANbU5Wyid5GK3yMq2to5XkBmYZPJuBSlnHU07zmh/kI5LsLgfLiOUVrFaK\nVeWaPfdstOvRXVP8ulyWlrOV5WhqOVKWJ4s7/Kg35jyFordyW+We1d7DLfSETKCa9u6I4jtIO0m0\ndNE+ZRWsLBHX7eJ1WS1NG+CIwPRxvq2+63OyAGf7QgSxECXvVnnXwha6JKRTw2ilLh7skm59hyt9\nh84BPaRJPVx4TqJcHiun7QVJRQatDUobdGSIqImoiKmJqIkpSCiIMSTBQkEblOkHf/uG4nbkbaNN\n9oFfsNb+OQBrbYVTG9uy2QHeQFu39YBtNNvVgMZ6h6VUvDTQLuAOHV/dFvcVUynKS8P6M40tNOUz\nsJeK1cWI84sTvrzUlBtLsXEBGzct52nP4aJzOxBNa8X5SjO51ExqTbpMuDw75HK8z8Uk5nJUsVls\n2CwU5aKAxQoXO0izstfKSsUweLALWqEz5yZtRMy6HVKWbnc+IsdVk0KpXaoiqHwb1DjgttBEnIgY\nUKYB78o2NA3QjkCQcoYD5RVl3Cnh4A3jv4U3lWPC/GUiJnwI3lKukE6RMoemO1xvF+t8BtMM1Awy\nyyZa8LwyxOWIVXmXUTYkX0K2VuSV8puSKsSlWN/IvzYyLC3jlWGMYVRYrtI7PEkmnMeKIl66SXhm\nYeHbZVtHXfCW8D5Lg04Sly+OYwnZHeBCPyUENICN0FBqRWqqAMQ11In3aaVQ++18JapMAFxW+8r3\nIbvQcloPPD59AAAgAElEQVSKli0qVbh2YdeSt5dJ2D97/vn80v4kddGIhz5NgJ7yhrHyPli1XXek\nElBxjY4rorgmiisSSlIKUgoSSvpo+lh6VPRxUT+im0tE/k8UvHHs+plS6ldxW2L9A+AvW2vXraNE\n874mgaNIzCVZvr3NaWzLbdhPd01iSGJ0pWk8W1mKC4UpFMW5Yj2C9UZzvhky2Gj6m1Hzgo3KYdCu\n2Ja2XieN7l72pI0mXUf0qoh0qYnOYzZpj03aJ0sTNklJVaypipKqWGGLpK041LDl+a+FS9ngmRq9\n/2bwfgk4iuZd1O4NITbxZnMEJvaTKN5R6QHEdsGkxm3V6YFPJl8jZQpM8i1IQhuMQkB8lXQnr5CL\nDKmjN5kMRNMOo2NkEAs/G5YzBO8ucIfgvXH+g6uMjco5NYaVGfHM3CGpDqkLRVUo6koFbkMX/9FE\nYoeO1LYkZU2yMqRFTbI0ZNE+Cz1moaGIVo5my3FvGirlGYViEqoojNaB5lVwE58UjWa7xAH6Pm7f\nmhOHUluahAa8RXGXuU9oB6WgEuVAQx2xfenq1gDyg07Zpmg6+H0b6y2DBprFOhnNToJdfwrc3C/C\nttU4VN7DvczyNsR9B97HwB3cTrUjYKiadT8967f1sdCz6LQkSivitCROSvpkrTTCMqJkhGbkwduV\nVNr/9eVtwTsG/hjwF621v62U+hXcq9B+uXVUtmPR5bXOeT0prVFKoyL/2RqUrcH43LoFwOJwsVuw\nkgromFi1xU6hmjZ+7qUGdA+r+qAP2Ea/aoVJVXtstvqA73TdgVZrWEew3vZc2oM81Bhukl2m/y7z\nPazDXed3PwdS1lAK/SRAFe433LGKWpqJ3K/rYAr4aRUFZm5YT7vohm4ZbwLIlwG35K+SzsBQytOU\nFqWdVqpthLKRNyqCTU6tPL/BXiuT9DuLKkooKpivUShqZZnqmpnqgT7BKou1EYYIqzU2CazNbUx9\nOBbCMvt6KmuXtvUg2+lCE9LWvUZYzwLggozana9kMcsh2ygNKzv3DXDgLSjWD+ZyD+ISySmr3yPr\nu4S/rzSTzO1djBWDUsZX2GUUXqnJ2PY1Vbi+pqzzt1zbBO8mSjEQJfXtx6sd4N7EcwL2LqRDB9KH\nuPeFvUd7jhsDAwNDCwMLA4PqF+heQdQriHslKWv6rBmohCERE2vYo2TPFkyIiGwzcRv7GmUO5G3B\n+zHw2Fr72/7vX2fneyx/M/j80Kdu5+fad4M9y/CgSb2sIF3m22SLiroyVJWhrmofniz+c+V1U1n2\n4KqmrxpLpxdBtR+7dODyFSNWtklmE2FXClbK5YVx0RRl5XIT9sAwTC3kF3fNAG8iEvssSUxZGbg3\nOWG69wongPB8yeW3rjYvf4cvteouZvHOGh3RbN/pl+Obwg+6MMgyHLGh9qc699Sde5nOdcKNfELt\nvjtpyHXb19Q9TXyoSQ4U8YGmlxjGi4zRYsF4mTFYFZS1oqoUZa0cpb9VGVxMtt2CudsAyr2zURJE\nA4gOLNGBRR9Yyl6PeXXAvNpnXh2wyYc+EEQ1e6Rf6zPdibIOfheLVPbdqLkeI/8yusdr4VEP4qFz\nZEc4RaSKPcXRp7XJlPQZmdAUbtIJWSxrGge2Kp2VVnntu1bet+UjVbY5jfYtWnmrSeU5fUozGNcw\nGcL4Pegf7tZbwvyaTqLaKT+E1b57vd5aO+CWCEPZqSOoAnJwwILXvhUmjqiTxBkniSbTFrSm1jGV\n7lGVPfJywKqcMC8O0FXN6W//Ps9/5/dpheW+hrxtqOCpUuqRUupr1trv4+K8f/f6kb940xVoUwGh\ntqwY7FccfVBx/KDm5MOK8WzF6PmK8fMVo+dL7LIizwxFbslrE7gs1DZ61PihZbxpsqfYpnGsyI56\n5B/0yT7okT/oc2YTzs0+Z2aP3NyivErgXGPPIjjTsCwhy0FlYDIwIecRmqbybCGneoMWfE26WrZs\nkStLgCOuO2pf55rhYJYBYGm40BDkuqZbuDJWHFedndK0dilSLjcrqFfu2nUYwyvl3hUZFN6/q+0L\neIeg3w3fIrhOCOAhFdJYGLqvSW9p+h8q+g8Uk2HB7dMF751OuX16wQEzNgVsCsWmUGR1E4stuezd\nJ6skE2SjXZcnA0VyR5M+UCQfKtbjPZ5kA57mCWV2xGZxDC+sexNsDazFQgvbt/tiu3CshP4PqZNu\nvYncgGwqcsCdFJDW/m1ikXsxQpl6B6PsoQ9i7bp3Q/p+ERpFJQ6Qt5suecd77am5WntqrqLZlbBi\ny3mHeWvYhOCdQ2Lcuv47A7gzgf2o3T12GW9dIz/UR2JgPobzfTgfwIVujNEQvKWa5fF6QKrc/lmp\nxuoIE0GlFVZHEGtMHFPGKXlckm0GrNZjeuuc3jpH5QbqBxx+45/bvlbzpe+3CeSrRJv8JeBvKqVS\n4EfAn3+z07sRCE0+2Ms5/qDg/rdzPvhOztHzOYefTTlMpxyUMwwFGwVrY1kXlk0dvhVLkWO9Xuk0\npUhZbgG3FJxoOIoVq6Mxq4cjVt9y6Uu7T1THZPUel+YO9bM+5osI+hHWRKAL0CswS7e3NJKko4ZW\nRJewexPw7gKX7Cd8SBOGF24otesaYd4dzKI+dBcdSNlDIITGFhYADF+cMATVd+aneNxj5bQ2rNO+\nFW6QtjbCetmE1gXxsDwhAIXX2AVSHVprO/E4eNW9mPQ9xeBTxfjbipN9ePDDko8GUz6qH3N785zF\nxsddVIoFAh12CyGiHIju7RQwtY1X6A8i+rcj+l+L6H0nYn70HunqFuUq5XJ1BBf3HFda4ZyMGNqO\neVk5GFoe4fOHlozU78sm4m59W1cv0R6kOQxq17yRry+T4r4Iwbtyn23s2t3qpgiiS9TGW15rsHPc\n3jqxO8fGDsSRBT5inXkNXKJNrhW3q3kncHQIH0zgZw7g1mg35R12k+7SgcRC6lNi4awHjwbQH7gD\nKtrgLXuvybY8Kc5hmVgffQKWCL9fI4aYOo0pk5QoNURJTbSsiGcV8awmnldul7qNhY3Brm3g9H+1\nvDV4W2v/oVLq54H/E0istdejTW4+u5OLuM422M85+mDD+99a88mf2HDn0QXv9c55rzzn9vScuihY\nGFgUzuKUJSGSxA8tKcZyX8H7Gu5ruB1rZkf7zD/cY/6dfWZ/oiKyJXmVcFXvEVd3KD8bofox1sao\nVYytMqhnkM9AixkpwC2DKwTu3W7Ol4uAjagJsqx4ggPvHu1lxbtednHNNtyRSpr4xC6pH5rZoQkf\nAuAYR/7tASO2kQWiyeAHr/LaNyF4y0Y/ryMvA5/uc3aP6dIvYi24FbG6n5C8B8NPFJM/DsfHJQ/6\nBd+or/j27AkPLj7jysJVpbiK3GaNMl2vcQu3uhFIA8IXPMNoEDO6kzD8Wsrw5xLO72iKWc7VPOGL\n2RE8u+8G69zAM6mndXAnCVkUjTxs75BCEY38LUQlEK0dePdrH/CrXURImbrJ2YbL2kXF9sCNbWgT\nqX5loc6d9WVmTuGx4Z7WMdcnKV+LttsnRcr28enILTT7YAg/+x68f3ydXeoCeahlx0DPuNQ3zvH4\nWEHf78O01q4JQvCW4d7dFTZSWwetMUCtUcbNYW5Gt2532x6oqXXvE7lwOXPjwjrlfYblHwJ4e/nL\nwD/GocsO0UEeDrRdNk0DIMVKsTjTnH8RMzpKKE4HLB9NmJ1VXCzAZCWrAtZV8+7GZmA18CCs7lZn\nsM4yvawVy8WY5Ysxy8/HLPdHnJqIK1OzrtfU9RX28QYex3AWYecxbHLIF1AtXWfcruIKaQCRkAp6\nE/Cmc7wQa7Lnb7icOOS8u+D7siTaumzNKSqJ9OxuGQSwwwiMkGvVjalbWz9wl446sVJHYayxotEm\nd4GvCo4JnW5dOiHkxF92vfC6DS1hC0M1U+RPFZsfKObnhrPPE758PqI3O2CZvce8gHnld4KkCUiT\n1MYGtY0SlqC7QRExuIrpP0kY/CDm6nKP02XCbFlRLOdwduZok7l1PpVW2FuYwpckG5qNnuTFFd3n\n7PoLfH3IApqWcReBHjkALleQnUGxwe2Jo8D2aZaorXwbhBRUxHbrC+Mvrgqopw1wmzXN7oLSj7rB\nuLtU5VA6fb+qHJV5lsGjjduitpaIKU/RdIdeyPaleK3b+GThTMFUwUp7drJytF9eOss7qv0j2/aj\nhF3QN+N2DvLuKitG3xx3j6nPlxbWpkmvw4R6eWvwVkq9D/xp4D8A/s3dR0kPCVfKhSbgLhsHNnPN\n5aOEKLWUG82LK8vkqWbytMfkcoxZ1OSZ240vt9ff/d11kUXWWaUvjN8cslJsLvtknw/IVJ/NYsBz\nm3JqShZmTm1OsRcp9rnGnkaO/5qXsMlcJzHha4zCfcFFQjB9/Zm0OUdAOdybQeNaf5dt2LVkXmY2\ni/aW0sy50jbd6AQBdgEKsTakXNaZvOJ4qsX5dAVmDnbpATyso5DGuQl4RVOWUaZolksJbIb7ZYgK\nFJKY3agUgrIbTB5RvtBsfqCwRqNHNemThPLxHvOzOxyuYta523d9Y5p3nYRLQQS0paYSD+Db1w5s\nNOnziPT7EWkdsZoc8mXW42xTkGUXMFPw1MK5N523jtjuncIUWmRhLLqI1EOgGqqgbpVqN3ekQKWO\ni85XUJdusVFhfZy4fxFCy78TTq6yelI14E3pJ2+ftnROOBmHE7H4XmB3f5a6EXXMT3aXOTxeu0ni\nuSwE6vncKyJhtwrXGYk+EiuINcTW7UH1zAN4piCv/HMswSzcpKRtE0UTPk7omgnnoDCQKwbWyu2/\nsVKONshss6eQfTO8+Cqa918H/m2c7XyDhHyjlF4ohTAWOHxqy2YWcfHYUmwU8+cx/XVEb9ajPx3T\nmx1AZihLqEoX1tqNAA/98aLrDazXiAz0K0V5mVCqhHKZUD5NWJAwNxVzO6eyYFYRdq7dQpqFxnms\nSiiFo9sVjw3tyr+JHnqZyLECNLKlqkzjYU/Z1Vt2AfYujUYiWHZFdoTgrWhvKiQDWbTYDGwFtXdk\n6doBt5n7gRu+Od3SHvSh+hI+l7w7UJLGrQETDTwPzu++nFr+FmJS3oUZ2tQlJtMULzTWRJRTTdWr\nKKcps+k+T6eG4XJEWbnIv6K+vnVSs3zKtUNov2ynkI0mPlVElSa+1OT9IZdlylVZkJUXbhn6zMLM\nuBmi5Yjt3k0+C/KIjt8NL4uDY8S5rNmGxWnVmecs2NzRHNXSveapSl2qw/cuyl4hMnl22szgOXAF\nqqa1DfE1cAau8Rtd6YJ4eKwH76vC9bHFHAYa7Nif5i2SkCkUHWRrFslvqsmXwJXXije4fZpL/+7U\n8sJZELKyWOPyrt7RNbbD7q5xi+G2q5lVew57Qz3vbVdY/hnghbX2/1JK/eLNR3b5RgELCfjscpXu\n781cU2QJ89OYKLVEdQ9djdGVQZcGjN36NsKoohCiuldtTZgWzKXGLhX2qcakioqYylZUzKlYQ6Wa\nTQ0rxXaTJ9m7+hposuPObyrd8yUuvKbZk7hLuHW18K6Eg6Tm+lLobu+Se4eDpztZhDsGwjZiwFS4\n3QW7b0YiuH44mYfPEY60hCbCRqJsBLhls6GwX4kTNUxisViaCbax9kwOxYuYahqRfRGx1pZZlRKX\n+yRlj6g+2i7ytbY99bVhpRm510iqjUKdgrpUqM/B6IjSJpQmp7QXzkIpjdNAqrANX5ZkFYwsnOkO\n4ZT2mu5ggpY9OFr6VA3luU8rl5s9sHsOBG2fRklZ4JCtw7GHi7isIJhxk/k1kA4AeCfdR5DvGl8+\nCXgv1nAaOwvCysQ/cIeH2+PIBh5h6io6srhp6xetIFvB5gqyU6jmDrClDF22b1sfO77fDh+FW9Am\nUTe+3Nb/9gbytpr3nwT+rFLqT+OXJSmlfs1a+6+HBw34u0jHSfiElK/7v8OVcW4n47Bx49ptwBNX\nEOdgUk2VxtSTiCr1GrNNKG1KYRNMHV3f2132A9/ufCadyf8tykRLTHCRV4mAjNduVNS22gVXuzNL\nmKCD+6ZzILgWFy60pP2uvjg4Vs7r+hfkGrsoLB9HLm/q3sYI+xlRCm7DhwjL6Xn38N2CVmKown2e\nVZCLdEGpa70032sNg37BoL9m0J/R611SZz3qrE+96VFnPSwDDH0sA+x243+3R4fdLl5pJjFdQ7yJ\niLOIGI2ONPXAUvehPoSq16M0qetjJqE0aTtir8Ahuwny7iReWQfKm10gnO/4znbar2uXa4j3XEr2\nId53bdcSccpKShvQVpHTMFtsUw3rtYttLnvOSbl1mlc72lHu1+1fAp44DVz2v9EWIovqVdCrfF7j\nXgZhUMqicQG9ch2L9pGCaqugofzdlEUpC2WM3UTYTYVdr50Wu6174yiOELgjGkNMnGRhVwMXJRWk\nNC8Z5Ev65TmD8glxdYm11iUs1rZrYFtGGveCMFVNk3orKNJuh08U6/pLNvWX2xa8fB344S3B21r7\n14C/5gqp/hTwb3WBG+Bb3Eemebd05vsIbSIbrNpAG7I+H8Uw7sEohVEPiv0em8M+64MBm8M+i2jC\nvO4xN3vM6z2KTa/tBFgZtwy8LFxedXnD16ydl4pM5T62IOq3o+jk1Xu7FgWGeym1lJDusv/uHi4h\ndyuu2O7gD2ePXZEmISgYP8hiXMifD5Gy0MTZGlAy+YUafBhnLea+eB7E6xBqVIFG1vIR7DDBtzak\nc4zGMRwdvuDOrWfcvvWEk8PnbF4kZGcJm7OULE+obUpNSk0PQ4qlwHoz3157yVxNimKoNEOlGaGJ\nehH5rZTsvYTsdsLmsM+86jGvhsyqA8pyzzmb5GUXcxyfIiseS6kf23nOLu3RtZS6qll3cg2RNnZR\nFuOxS6MxRJ7b3Wp7IZXkKSm/4932FmEXUQqmPqKpFF+ClElmKmkv0eTZ0Z8IzvOXib252zeo4wp1\nXKKOK/RhRawqt1GTqoiUo07digwH67WJqExEbTWViTwIWr/pk4WVxZxazHOLee4W7jUW6hroX+9u\nYWBYGGW7bbIB7n0CLg2qktvVgtv2nDvRY4a8oDZQG0ttgnXdKui9yhs3Atp+zsSvYdty7BJWq32j\nqPe3iP+rP+K15KtGm4SPf00+5Atk4ya37qxh9W2QQF4B5aJmj2I46sPRCI5HsL4zZnZ/wuz+HrP7\nlrNkxIuqh60OWFe3KaZDeKrgma8UVUPm+bk63BtQtNg/CBHwPgKOQY+brREO/E/S90VTCz2robIp\nSs2WI5R8QxNDIxugCKhJxMUuCUEgHPwJIYBB7XtfDLoP0RhI2xqlaGBbEzhcihw+QBgAG5J43TKG\nJscuS0HRMMwuvDCODMeHZ3z04JRPP37Mw/tPmP8oZpFGzPOI+VlMRUxJRIn7bCSqZLukJgzdNAwU\nHCrNgVIcKkXaS1jeGrP4mRHLT8fM7sPz4pAoH5EXJyyy99xiGkng3mG38dZGVTYW3rZ+pU5Ck1C+\nlzroRs102yv0sPWh14e9Phz34XjggKCl8Hd9TFGjFkoWYq5R7trVxG3vwIBw1YTLJdZfwLs7wXTo\njharZVETi7pToj90KbpfkqqCRLk8VQVO/9bbvDQxZZ1S1AnGJKBAy059UY29LKi/78pn5xvsTJy9\nfmdOm7bHmNRRGCgVisVRRebIUxoDBrbkDks+5ZyvxY851E/dPF1BaS3GBpQ5/rMOksJvVuUNpBi/\noEd594wH73AG4CcM3kqpD4Bfw632t+x6EQPwkM+3bKD8czNs95+rPev/vxvD3T7cHcPdA5jfO+D8\nk2POPrWcfxoz6BlsmbIq9rko78CLfZh4UyTXzsvEwoWsld3tLa9xJW8pIXjfBX3YgPeJz4WaEdBe\n0SjLoWd1O/WFdp28usnSOAhl8IdaT5caCSM4hNoJnX/NetSt+qFjZzlEE1wwKk2hjME5aCtQQouI\nFt3WkK/F7LqL00aXUDPtcpwqOEc0+ZQ4Kjk6OOPhg2d891uP+fanX3KRaC5zxcWZ4kJpcqso0H6V\nrcbsUBDCyh4Dt1HcUXBbKfq9lKtbR1z9zCGX36s5/zQl2iiKbMRscwKrD9z7ozyd6qh3/4q5yq+8\nbWnX0teEQJW2CjfREkspDEkIna6yb4nsgjR0i1P2EjhJ4H4Mqe482i7KRap3B6daAVXPAXc68F+c\n00w4UxrQHuA0k22oBu0Q0qAZBe/7wNig7pboTwqib5bEXytIdUZfZfR9bjxoGyIMmrzuoaoepupT\nVX2sxu3UF9VEcQ3PVsAldpZhHou2vQrKFF3vbmE3Dd07Wzn2/HMf7D4DXXI7WvC16Iyfix5zRz8i\nx25fPOWXKzWh3h6LI+1T5ME7bZIso99a6d0XCOxoopvkbTXvEvgr1tr/Wyk1Bv6BUuo3rLW/Fx40\nYH3dicPNbhl8PlIwiWA/ditgdT+nGBZkk4r1Qc2gb0kLTVzEqKIHWd/t9NX3PFJUQVQ43qsVciZe\n+jD6oXv3MH+ZSO/0GpHqt6O0ely3nsO40C5ebSU8oUc7vonOSd0W74aJ7XLmyezheRvlW0PJZ9Mp\nV9j7Q43SmxSqhKSGxKISDUlEUhmSqiapSpK6xBrHExrrXu786hqX0e/SWJccD55z7+AFH90542sP\nLnj6PCF9kmAOErJhSlRBXFsiA1EdLlq3mB1OL1lQM8GyBwx0jzodUA6HFHsl60NDrwfJRqPTxE1u\nI9oLDiN89IHUi6bdp0JnbKiOBpSTStopbHPVd6W0gZctUS4mWebitL4+N75Kut1+2ycVjd9DnMOi\n+MjmV9LvXwHekmtclECawKiH2qtRhzVKb9AqQ+sNkc5Q6FbSdR9d9VHVAMo+SiuIK1RSo+KKqNDE\nx3PUSYk6nsNs6vdPadKW/bNsF212RbU+b3CcukKhuZVccJIsOO7nHPUNh0C2seSbmtwYqtoEa3Rs\nC8Rlq29Ho6jmfQ0R7scEvy+4r/ct5/Ia7Re0xBuLtfYUOPWfl0qp3wPuAS3w/pwHu9waN4K2fF5V\ncLmBZ5F7Lefq6YRptM9Vsc/VdMiLRHNW5SzKGVX1DC4X8Fi7FVKXyq1Y2qyhXNPEZAsYxji1uO6k\nrjcxHAW7uEkhtedABPWiUZbFORI6twraL/oIlbTtrUKtONCMGeNGatdTLyeGGmsI+ALe4d+hvqBx\nkSJLF+onJnIr8CEAbCugHZRTF6h94DhBHSWo4wkH80uO55ccz6cczy+oi5q8tBSVe6t5ZbuxH10Q\nD2mfiImtuFWdspdN3cZkc8W6PuCid8zjwxN+9P4JxcpSbizlxlBuLMZUuDePukmm6xQfYpljObeG\nx1h6ecL8bJ/5j/aYpxOmFylPc8NVviLLz2ATwzPgKY42WQDrwofW+Y24rk2esjxPLKBwQZfxjxn7\nFLlcpaADMLduRaiL/NBOKckyF+NM5qJFtl3BBtq3yA40CCu7Bs6t67e5/HCJe0uILMoJ1xpUNLOX\nkOede4TGmAKWGnuaYkcpdZ3AVUqhQSmN0TGVTh1dohRWCW2SkNc9qjrF1DEohYoUVRxhI7ft8sE0\n52A45eDTU0b7L5yRMFMwU9ilchvW1c5nLKvOuzXTmmf0lEhfofUpkf6C/WHB5GDJ9OCAf3TwHX5o\nH1BcbiivMorLDaYoHNFjDZGyaGvRppmvtMVPBAqMQlUKauXCBTPlVnFGgTNZeWcmf/96m+2Qr8x5\nK6UeAt8D/o/ub1/wQXNckN+kdW27TgXPMr/1ewWZGrDOh6yuhqyeDJlFmitTsKynVLVyL969VHCh\nXL9bGTeoipxmY6SQO0xpew67jsKQzwgBMkyxP3YBFGBiB8zykuolbV+V0J+CfcI4tByW0uPlBFmM\nMfa5DJ4QiFqtQTsssxta2A3NU44SMX5LO3vlvwvKhmUbpbPltgOHqjZwMEB90Ec9HKAeDjg4zXjw\n7AUPn1/xsfqSYl2yymCZWVZV+z3HEsfQroo2MTu2hlvlFZNsSrrKsHPNuj7gvPchjw4/5vvvf0x5\nZamnNbU1VFmN9T4D29qov6nb1Nac4V5qMMES55rN2ZBNMmCTDVk96nFV1kzLJVl15mL8r3AAceXb\nOfeB4JVMbF5T3tIMsrBJnIh2+0yg3GCNnLVC6nMdORpL+8829cCdgo1AV5Av4WoG6xnoMgDubl+F\nneANTbc2wMK6lMsPEuYZgneojYcmZIeakUsIBW3BLmPs6R7G7GPne9gnLgLG6IhKpxS6j1WqlSob\nUxmXrImxyhHKNjJYHRHpiGMKHo5mfPjJKSf3H8MTnO9LgS2Ui9H3xSjMbpIu7GmxGhLr58TxhCSa\nUI8G5Mc9prcPeH7nNqWtqHpzajOjWs4wrNG2RikT5M4Qc+HgAtwaVbktrh1wR85XkUQetENiXPOH\nAt6eMvl13IsYlt3fP+dB+3ifdy277t9p5XZ87JWQbqDKYsqrhPJJQjmIybUmMzmZnVHZHIq40Wo3\nyq0Oq2uXjJizMoAOaQjpUNPtehdvAm/psNJD5y43pqF7ZdvhrjIfMiK7FOgWalocEMiugsf+syC/\nTErh1CeTSncL1zAVtDWmylkntnAUCKYzm/oytcIFAzDXCnUQoz7YQ39rD/WdIw5+/IIHo5pv6ynf\n3XxJrgqmWK5quMqbdanhQv+QmGm0b5eG1nJSZexlGb1ljl1o1tUBF70HPD76Dt+vvkcdG4ytsFmN\nURXu9dFhvHnWumtERUpNYg2prdE5VGcx1SahfBFTDlIyU5OZJZmpoJ63X+uYgQ898GqdwU2y0FAP\n0g69oA8F/LaKnG2dKuhp58CKvNNdchvhlnprlxsfe7y6APMc9/alsH122bo3iJxS2Ca5L2jHRcqk\nLablK0ha6SKSVynW3MbMY3g2xgxTjIo9cLvXhqHxAO1y2d9aEsrtj26023892tMc38/5+N6U7947\n5YH+0g0TBawUZuagIMOtf8q4GbRlRCcqIY1SenFKmqScj+7yw+NP+NG92/zw4SdcmD6mPsMuzzBn\nZyMazLgAABSaSURBVFhmKGqwFUrVKFu7MHChSwyuzaoIpSVUM5ycfYiu6q67eD35KsvjE+B/AP4b\na+3OPQwf8YPgr4e4F/C8hlT4pbledm55JQNx/hoXlM2dYtwAO6FxDobL3AXEdwVpbwk8n4vnw4OD\nyf088AakFXDdkAu7lMzEI1/mUVDGcH+TcAYIgXpXTG7IweI0b5a41ZCyf8qbFD+G8R7qdoT6aIT+\n9jFj3ed2XvPxfM4fefGcTZFxVsJ55raPkAXT4XYGNxFWAD1r2Sug5zepy656zPIDzqL3eTz+Oj/m\nj2EK49ayX/mFQixwHUfS+vpdbbCKsTBwgUsttVR2zdk+8Es+WxqfigB1tz3DgZq0v+6rptlkfg1n\nNWNhnUO2gNUlrE6hltClLnh3Hdovk27td1vgDfuEgLa4lvI+dpVgGSNRUrXWL49i3VXs7XsxQd+1\n7A1z7j9c8PX753xt79Q17RR4CiZxBviqdivRJdYsTN3b9xT0taIXQT+GHw02PNq7x9WtPX7v3tf5\nwhzB1VM4fQbJHq6zePvRii3Zqb46pJfE5xFGFPWAHwD/kPZCuVfL20abKOC/Av6xtfZXbj7yz/i8\ny26H0uWWoW1I3+BpeCMxuAG7oNkfpBuWF2rdEoTd7chhzwoXMITgHibYTRjJs3YaVkUODLXP7Rjs\nBLeVZuG0r22URneFZRhXLXUXPofUqey9KGaxgNpbrM8FByjTHPtojpm48KzpF0u+/Eyx92wfNb1P\nviqZZTCt3GrwDZoCRYmmQHs9vu1kDOs/NZZsCdMzOB3AfpHwu9k+jzaK2WaB3TyFM9wy88x4LTTY\nS3rbh4RLF83YsDN88sYl292BGMbTRzQ7LQ5pNo2SSVbaJ4yGiBqmTANWXb9sl1rLLqCYueXs2xWF\n/197Zxcj2XHV8d+5ffu7Z+djvbveHa/YBBPbcRzbMbINAeEgE2wJghQhkUhBEQ95QgTxgDCIB78h\nJBAgIV74iCBSEolgkC2hyAYcKShSYid2svnYxBt/7m52e73emZ7unv669/BQVX2ra3rWMxOP5o64\nf6lUPbe7p8+tW/WvU6fOORW6G4ZyhrSFR47KdCmhoR/rvJWn/z2/vhHccnQdaGffmeZDsZciu4KY\nWYl68EQY9tu02wPO/6hKPTpGuzEwiakvmp9Jhyb30SAx6UN8fx93F+F8EStUEjErf+BS9wTnrzZp\nV2GYds3zutA3kZ1DN35Trw5dWHw+c7/qxq2f+uF9wF3ejf7LDtp075r3B4FPAN8WkRfstT9W1S/N\nfsztUOucwjZ1OG2/E+StGIJyWrojqzAril/mdU5fo3FGPaf9QqZG+WaJsMv4/9e3T1tfoqgCpbKp\nXZKdNLa2e/cbjoAga6uIWTOKv271Szcozpzg38cukCq6PoA3NsyfnSFrV7u83o6gfYTO+i2MNxP6\nA+iNhb46o0VWzFlH5pyaWdc+Q6pxoqx14XIbllJodGJeGy3y+khYH3VhdBHWI5OHZiCW1JzPuU/e\nru2UWc8Q115h9FSIqfMys/sKrnZBW3WyzUpnM+7bNg76hGcbJpGtOkA4bMZdGG0Y98SZPY7t6tA+\nHQXG39QsZ+hgJnXXD1wf97X5sB/P09LnIbVt4PLTuInHCpGCOdxBTZ2GfODLa+pB7zrtKwNeiiqM\nNo/xalXgMshl0DWj64wnNi9NOhvQv535pJRCnArxREyequ4JXnuzRTuFQX/DyHelB2+N7f6Arzz5\nD4ygToNrzpTWJFMkXdl5HMpevU3+lx3p901mNb9wNt+O0P1IwlBj3QvczI/932430de45hmkYfa3\nw87rlt1OTqfxuMETmkHC+w3c+KSGCZapGtc0jYw9VVOQkV3mV73fickmHieXfy9O+/ZXFCF5uw7j\nE/8u4DRvOtAZohe6rPU2oCt0uke40LuF1HqajCfCSIWEmJQy6bQ2cvuHjPn3EKUp9a45t6DWg0o7\n4npyhLVEWE82YHIJhjEMYrMZlMbes3Far3t+zqbokxJkfUTJgmh8uO/64ee1oFS912WyAdnDEJdL\nkmXHg+qsjjKWt5/vk7EtE0/zdv1onixesI6/LJ+mh02AN01fkwS077WXTzx+X/YF2sn4dNr9mm0P\n637oH3A9dVmd53+kmQgWw94m7faA8WaVN6/eRDNuZAvKDbOIcNsR7kDx0BLjN7FgFP8oESIVokTo\nd5dY0xbXezC8Zk9i6PahO5pD3r7cPlmH1gT3PefD74/PDXZjovpJbN6PAH9tJfkHVf3zrZ9y+aLD\nG4StRBYSuxu87wSc5j3GDCR/hgxlmWd19ZeO8667JZRzCfOTcPk25vCeGxhNzYbYSxOiOpQaUG7Y\nzdYe2bFikD34svcb/ij3O5Czy/vF9XBXfKLaG3nr2hA6IzTqQiSsaUonFS7qIlG6gNoBanJVGMLR\nmSyA4Puf6JR4DZlLkiJdkB5Tb6qUCokKKRuoDjHeGF7ZQjKQ6Rt+7YprN6cKz4NP3s732g+imWbn\nJ9scdn3uOll726KJN8eG5oxtoFNnYUzaU5eIyWn9LiGVn6chiLh0ECx5Wy8m3SRb0bk+NM9S7L+3\nG817ZNugxPSw5WlSK48fZgh8vnY/SFLamylvXq1Qio4h6BYdUcm+Ok/KLU2tYA5SMDLppEbSb9pc\nUnaVmo4xHlow65Xg2jFsl3ny+5r3JpkS5VbCO8Nebd4l4G8xZ1deBJ4TkSfDIJ0s+c48G7Z/U6Gt\n2IXi+tqwb9KYt8EyT1PGu+bLEM65rlMC09Sn4e7J2ywdpWS05ahu62rmMRBZl7Dp2Xx2aZjWIalD\n2jB1VDeaN1VIykbjTt2g6pDZ2d3hDH7Ah2/68dsrCM8uj6EcQ7kF5Rr1qM8CG7Sky4JsUErGpCOy\nkszXLfwWN8vNjLKShTJJKyZplUkWYoZpncGkxnBSZzCuk46rMCzDqGzqCXZAjMkOLfYd5CeYI0rU\nqFMzZg8XyRj2lXkTrV8coTlNFTJic+3pfy/sS/P6Ztg6/sB0WnLsfQ5zXcn61fS1j3DfJFQa3ETt\nNHv33F0mSkfafkCN/Z+agK5ZOV0KhlD79VeVbnzMU3Ts6ygyfutxDHGJUhlatR4LtT6t2hrNSt/u\nIgrSN7XaSBq1ueFN4qcsAVQ4rqUM0rKlCZNqma626KYLdNMW/aQO/cSUXgqbswrB3NVVpQK1KlSr\nptYajOswMoFCTEp2dTLJap0wjUBWn5u2aZ/4CJSXTWKxcgvKZSinJmCnXIJoFETLbI+9at73A+dV\n9VUAEfkC8Bts+Vk/ddd2JhMhc5Epe699rdXZL513SEhUoYZ+I23Af883aYSbUHHwet5mpK8tRJiD\nXOumVKrGd9flMihLtn5za7lxFUYVW1etxmj9eSdiNbMh5hipNTLvFqexOvPAvOLIJfDLLpdtxq8W\nNCs047c4KQNWoz6rcpnasMe4C+MNk6donGzdDQjXUNWgjI40GK66Umd9UmNts85af4Xx5gpptwqd\n2JaS2VGaeMEu040z/wSZcPC5Zxm6Szoim6fC+v3PLVtrmNSqPnGHG3f+YHRy+C6Xrp+mWTvPBDM5\nn32XRNp9fl4BtigWvsLgr6bcCiEhGxdOe/bHkj+e/EkCK/M66DqZX3c4TYeeMr7FdI5tR0pQqUGt\nBrUqcVNZWf4xq8sdTq1c48RCG2kDbUHagrRBExt9myqapFa/sVG5ChqM6agK0TGITkLpJAwWG1yc\nrHIpWeZScpT+4CZoD6E9MmUzXIHOIe9qGRZbsLwIS0dA69CtZGUYWRfk1JbEjNHUuSS7vhhO4l4p\nN6C5AA2bWKxZhUYJmjVoLBjlap/JexV4w/v7AvDA1o/1MW4wt8IW4nZws7rTTOpsScZDnxmD1nQj\nKtSAb7ThFhK6+24EvALcztYNH7+EXgYwQ5aOvKsNqDWgXrOmUTGlhqcgq02OUzJ22s3Y+HxOYuNa\nlJTM5lWaYE6q72Hshd8E7mQ2LNknmHDGn2PTLy+aDrO8BMvLNCvCyajNbdEm740u0+qvMbhmDlMZ\nDmAwnPXLGc3eNcrsgv0SPVaPnKJ/epHeHWP6d8CV0RJxp8a4s0yncwqu1aEdmVQGackMABkyPRSA\nAdkBgm6C9Qn5Rqa3CTfejlGMW8L77f+rY8jbRZ+6PBlOQw5/x5G3I2d/debI04/qdcqB6z/hZmeo\nNEBG1s9j9CTf3uxcOt14cPluHOm68bSdH55kOtNUo7Uyq8vPEipaM/6M3v8X4Bzw3tl2ispQaUHT\nZEAsLaWsrHY4c2rC7atvcevRV5FXQBpAKkgHmw5eDYmjngu9ksyxzkgV4psgfjfEt8HG8UVa4xV0\nHLM2Pgrd0/ByH6QP176G4aCeldk9l+CfVsuwtAA3H4WTx8yK+K0SvGXHZ1dMuOZYTS1uJZgyPWBg\nC88FY7FcM0S95EoCyzVYasH1r8BtPwtfYEfYK3nfSLX10MM83FM3+GqEIWl/82WBzBa8gOmoNSuu\n63n+0s2Vt7PBzSNwAV7GuOpMM8ewVZ/0A19cs3n6qAjETag2odGEVs27BTHMNrVgqN3HEnNenjuB\nBMGcGyhG805Tq4X2rOb9InDMk3ueXfdGtUK5acl7GY6v0qwPOFkqc3upz/2lyyytt81xepvQX8sc\n7lxxOovvk7PolZcYcs+isH56zMad0HmwQmUA42s1Nq4tU7p2Ci7ZHB0p5lio0cQQRzKEaABJn4zY\n/BWaT9DePc30Cbx6O5wD7sD0PUfedTLi7pBF0PpKgf/7Y2ZJUjCE7btiusnBzxVc9167Pr2dh9Ln\ngI8yS8JrWGd0ZonbTa/+qsRhTrvMDIVwkzvsOyF5++6JPwIeZOZZRBWoLEFjCY4sEd+UsnL6Dc7c\nOuH9t17j3lOvIg1j2pYOyCVrorPdP9Fs12OipmxhjSqUj0H5p6F8N1z/qWOkwzOsD8u8PjwKa6dB\nNqC/AWfPA/eREfc2yemqFUOiJ4/Cu09B0jS+95Ebj2TztTihbC1zzEfAFgWqHEMjhqUYjsdwXOF4\nCscUnj0Hd390vmxzsFfyvghe7Lt5fWHrx/4Lk50kBc7YEmLeDO9I3G0GJRjqcNrKdj6sO0XYsV3x\n7Xp+Mit/197VMGO+EcEkp6pBXDOmE/dVN1ad2L6iNppzfapI+mYA587nTqbZIyK14dhVaDSJ6zWa\nJWGxlHAs3mRl3KVXhV5s5hWXHNSV0GiVYvLMLWFiVxvA0UqfUmtIaXkEJxKam1CTmFirSNKEjWaW\n5KkMJpGYHSRANsD8cH6/gfxGCrWdncC1q3vm4QTtk3ISfC9c0fg2VD/3b5+sP7nUBs406HeMMCLW\n748VTEOFJsRwVeJWBL6r417a5UYIzYth4InfPhU7DupQaRLVEqqtmIUlZeXokBPHN5BliBbMkJHY\nLDKT1NZiyhjDme6OfJRKRomtLED5KJSPN1kaTGgMhfKgCqUWLCRQn0DknrH/bOcgikwCrUYNFmxe\n7w6zw9/tS7rmcN1oWwQr3ygy468amaPbmgJXvgzf+jKc/yo88Rc7ehpsfxdvi+eBnxGRMyJSAX4L\neHLrxx7CELarCxQoUKDADG59CH7zcbjnIfjU4zv+mqjubWYWkUfJXAX/UVX/LHj/nZryCxQoUOD/\nFVT1bU0JeybvAgUKFChwcNir2aRAgQIFChwgCvIuUKBAgUOIfSVvEXlERM6JyEsi8kf7+Vs7hYj8\nk4hcEZGz3rUVEXlGRH4oIk+LyNIBy3haRJ4Vke+KyHdE5NM5lbMmIl8TkRetnI/nUU4rU0lEXhCR\np/Ioo4i8KiLftjJ+PY8yWpmWROSLIvJ9EfmeiDyQJzlF5Dbbhq6si8in8ySjlfMP7Jg5KyKfE5Hq\nbmXcN/L2QugfwXjxf1xE7tiv39sFPoORycdjwDOq+h7gv+3fBwl3RuidGCfa37Vtlys5VXUAfEhV\n7wHuAR4RkQfImZwWvw98j8zrLG8yKvCQqt6rqvfba3mTEeBvgP9U1TswkU7nyJGcqvoD24b3Ypy7\n+8C/50lGEVkFfg+4T1Xvwjh9fGzXMqrqvhTg54AveX8/Bjy2X7+3S9nOAGe9v88BJ+zrm4FzBy1j\nIO9/YPLI5FZOjIv3NzAhgbmSE7gFE3TwIeCpPD5zTJjv0eBa3mRcBF6ecz1XcnpyfRj4St5kxESo\nv44JjYiBp4Bf2a2M+2k2mRdCv7qPv/eT4ISqXrGvrwAnDlIYH8EZobmTU0QiEXnRyvO0qn6d/Mn5\nV8AfMps/IW8yKvC0iDwvIp+y1/Im47uAqyLyGRH5poj8vYg0yZ+cDh8DPm9f50ZGVb0I/CWGwC8B\na6r6DLuUcT/J+1D6IKqZ9nIhuz0j9N8wZ4Ru+O/lRU5VTdWYTW4BHhCR9wXvH6icIvJrQFtVX2Cb\nWLiDltHig6p6H/Aoxkz2i/6bOZExBj4A/J2qfgAT6jmztM+JnNjgwV8H/jV876BlFJFl4CMYC8Ap\noCUin/A/sxMZ95O8dxhCnwtcEZGbAUTkJOaspgOFd0boZzU7IzR3cjqo6jrwLPCr5EvOnwc+IiKv\nYLSwXxaRz+ZMRlT1x7a+irHR3k/OZMSM3wuq+pz9+4sYMr+cMznBTILfsO0J+WrLh4FXVPWaqk6A\nJzBm5l21436S9w5D6HOBJ4FP2tefxNiYDwwi254Rmjc5b3I74iJSx9jtvk+O5FTVP1HV06r6Lswy\n+n9U9bfzJKOINERkwb5uYmy1Z8mRjACqehl4Q0TeYy89DHwXY7PNjZwWHyczmUC+2vI14EERqdux\n/jBmM3137bjPhvlHgR8A5zFnXOZhE+PzGDvTCGOT/x1gBbOh9UPgaWDpgGX8BYx99kXgBVseyaGc\nd2Hy1H4LQzZ/aq/nSk5P3l8CnsybjBhb8ou2fMeNlTzJ6Ml6N/CcfeZPYDYxcyUnJpvXm8CCdy1v\nMj6OUXTOAv+MSX21KxmL8PgCBQoUOIQoIiwLFChQ4BCiIO8CBQoUOIQoyLtAgQIFDiEK8i5QoECB\nQ4iCvAsUKFDgEKIg7wIFChQ4hCjIu0CBAgUOIQryLlCgQIFDiP8DD/RVkrAIhzsAAAAASUVORK5C\nYII=\n", |
| 645 | 706 |
"text": [ |
| 646 |
"<matplotlib.figure.Figure at 0x7fe7fbb4a438>"
|
|
| 707 |
"<matplotlib.figure.Figure at 0x7f1b8e99ee10>"
|
|
| 647 | 708 |
] |
| 648 | 709 |
} |
| 649 | 710 |
], |
| 650 |
"prompt_number": 68
|
|
| 711 |
"prompt_number": 17
|
|
| 651 | 712 |
}, |
| 652 | 713 |
{
|
| 653 | 714 |
"cell_type": "code", |
| ... | ... | |
| 658 | 719 |
"language": "python", |
| 659 | 720 |
"metadata": {},
|
| 660 | 721 |
"outputs": [], |
| 661 |
"prompt_number": 69
|
|
| 722 |
"prompt_number": 18
|
|
| 662 | 723 |
}, |
| 663 | 724 |
{
|
| 664 | 725 |
"cell_type": "code", |
| ... | ... | |
| 669 | 730 |
"language": "python", |
| 670 | 731 |
"metadata": {},
|
| 671 | 732 |
"outputs": [], |
| 672 |
"prompt_number": 70
|
|
| 733 |
"prompt_number": 19
|
|
| 673 | 734 |
}, |
| 674 | 735 |
{
|
| 675 | 736 |
"cell_type": "code", |
| ... | ... | |
| 680 | 741 |
"language": "python", |
| 681 | 742 |
"metadata": {},
|
| 682 | 743 |
"outputs": [], |
| 683 |
"prompt_number": 71
|
|
| 744 |
"prompt_number": 20
|
|
| 684 | 745 |
}, |
| 685 | 746 |
{
|
| 686 | 747 |
"cell_type": "code", |
| ... | ... | |
| 691 | 752 |
"language": "python", |
| 692 | 753 |
"metadata": {},
|
| 693 | 754 |
"outputs": [], |
| 694 |
"prompt_number": 72
|
|
| 755 |
"prompt_number": 21
|
|
| 695 | 756 |
}, |
| 696 | 757 |
{
|
| 697 | 758 |
"cell_type": "code", |
| ... | ... | |
| 702 | 763 |
"language": "python", |
| 703 | 764 |
"metadata": {},
|
| 704 | 765 |
"outputs": [], |
| 705 |
"prompt_number": 73
|
|
| 766 |
"prompt_number": 22
|
|
| 706 | 767 |
}, |
| 707 | 768 |
{
|
| 708 | 769 |
"cell_type": "code", |
| ... | ... | |
| 713 | 774 |
"language": "python", |
| 714 | 775 |
"metadata": {},
|
| 715 | 776 |
"outputs": [], |
| 716 |
"prompt_number": 74
|
|
| 777 |
"prompt_number": 23
|
|
| 717 | 778 |
}, |
| 718 | 779 |
{
|
| 719 | 780 |
"cell_type": "code", |
| 720 | 781 |
"collapsed": true, |
| 721 | 782 |
"input": [ |
| 722 | 783 |
"def extract_chroma(audiofile):\n", |
| 723 |
" data, rate = librosa.load(audiofile)\n", |
|
| 784 |
" data, rate = librosa.load(audiofile, sr = None)\n",
|
|
| 724 | 785 |
" out = vamp.collect(data, rate,\n", |
| 725 | 786 |
" plugin_key = \"nnls-chroma:nnls-chroma\",\n", |
| 726 | 787 |
" output = \"chroma\",\n", |
| 727 | 788 |
" process_timestamp_method = vamp.vampyhost.SHIFT_DATA)\n", |
| 728 | 789 |
" step, chroma = out[\"matrix\"]\n", |
| 790 |
" print(\"File \" + audiofile +\n", |
|
| 791 |
" \": sample rate = \" + str(rate) +\n", |
|
| 792 |
" \", chroma step = \" + str(vamp.vampyhost.RealTime.to_frame(step, rate)))\n", |
|
| 729 | 793 |
" out_file = open(os.path.splitext(audiofile)[0] + \"_chroma.csv\", \"w\")\n", |
| 730 | 794 |
" csv.writer(out_file).writerows(chroma)\n", |
| 731 | 795 |
" out_file.close()" |
| ... | ... | |
| 733 | 797 |
"language": "python", |
| 734 | 798 |
"metadata": {},
|
| 735 | 799 |
"outputs": [], |
| 736 |
"prompt_number": 75
|
|
| 800 |
"prompt_number": 24
|
|
| 737 | 801 |
}, |
| 738 | 802 |
{
|
| 739 | 803 |
"cell_type": "code", |
| 740 | 804 |
"collapsed": false, |
| 741 | 805 |
"input": [ |
| 742 |
"for file in glob.glob(\"data/Music/*.wav\"):\n",
|
|
| 806 |
"for file in glob.glob(\"data/Music/*.flac\"):\n",
|
|
| 743 | 807 |
" extract_chroma(file)" |
| 744 | 808 |
], |
| 745 | 809 |
"language": "python", |
| 746 | 810 |
"metadata": {},
|
| 747 |
"outputs": [], |
|
| 748 |
"prompt_number": 76 |
|
| 811 |
"outputs": [ |
|
| 812 |
{
|
|
| 813 |
"output_type": "stream", |
|
| 814 |
"stream": "stdout", |
|
| 815 |
"text": [ |
|
| 816 |
"File data/Music/07 - King Henry.flac: sample rate = 44100, chroma step = 2048\n" |
|
| 817 |
] |
|
| 818 |
} |
|
| 819 |
], |
|
| 820 |
"prompt_number": 25 |
|
| 749 | 821 |
}, |
| 750 | 822 |
{
|
| 751 | 823 |
"cell_type": "code", |
Also available in: Unified diff