reservoir-dynamics-impulse-git.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import pylab\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import csv\n",
    "\n",
    "import scipy\n",
    "from scipy import stats\n",
    "\n",
    "res_seed = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Numpy backend.\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\PeterDell\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "#import cupy as cp\n",
    "from easyesn.optimizers import GradientOptimizer\n",
    "from easyesn import PredictionESN\n",
    "from easyesn.optimizers import GridSearchOptimizer\n",
    "from easyesn import helper as hlp\n",
    "import numpy as np\n",
    "from wikipedia2vec import Wikipedia2Vec\n",
    "\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "\n",
    "vectorDim = 100\n",
    "\n",
    "numNode = 1000\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cos_sim(v1, v2):\n",
    "    return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "10000\n"
     ]
    }
   ],
   "source": [
    "#reservoirStatesBuffer[:,:resn]\n",
    "\n",
    "in_small = np.load('in_small.npy')\n",
    "out_small = np.load('in_small.npy')\n",
    "\n",
    "print(len(in_small))\n",
    "print(len(out_small))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki2vec = Wikipedia2Vec.load('enwiki_20180420_100d.pkl')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 100)\n",
      "five 0 thousand 1 feet 2 albany 3 perfect 4 day 5 beautiful 6 blue 7 sky 8 cloud 9 sky 10 see 11 horizon 12 every 13 direction 14 uh 15 girl 16 pushed 17 airplane 18 screams 19 wait 20 wait 21 chute 22 gone 23 plane 24 think 25 sentence 26 ends 27 know 28 functioning 29 properly 30 great 31 jump 32 think 33 ends 34 worse 35 know 36 lot 37 skydiving 38 would 39 think 40 know 41 working 42 chute 43 lends 44 better 45 better 46 jump 47 especially 48 towards 49 end 50 happened 51 forties 52 suddenly 53 surprisingly 54 want 55 one 56 kind 57 uh 58 midlife 59 crisis 60 guys 61 runs 62 gets 63 convertible 64 besides 65 needs 66 two 67 convertibles 68 know 69 already 70 gotten 71 divorced 72 already 73 checked 74 uh 75 needed 76 something 77 went 78 like 79 cliche 80 n 81 ber 82 three 83 drive 84 upstate 85 skydiving 86 academy 87 uh 88 near 89 albany 90 meeting 91 meeting 92 annette 93 school 94 bare 95 bellied 96 khaki 97 shorted 98 know 99 tan 100 shouldered 101 senior 102 instructor 103 nineteen 104 years 105 old 106 like 107 petite 108 could 109 folded 110 put 111 pocket 112 looked 113 like 114 lowercase 115 smiley 116 face 117 dot 118 head 119 made 120 feel 121 like 122 capital 123 standing 124 next 125 professional 126 first 127 thing 128 shake 129 hand 130 put 131 arm 132 mine 133 squeezed 134 little 135 bit 136 said 137 going 138 awesome 139 led 140 office 141 looked 142 like 143 kind 144 delivered 145 back 146 flatbed 147 truck 148 like 149 dropped 150 corner 151 unloaded 152 go 153 inside 154 office 155 first 156 thing 157 says 158 pretty 159 eyes 160 hands 161 liability 162 waiver 163 extremely 164 darwinian 165 moment 166 uh 167 done 168 know 169 drive 170 away 171 something 172 well 173 adapted 174 like 175 know 176 driving 177 signed 178 uh 179 told 180 pretty 181 eyes 182 wanted 183 know 184 pair 185 pretty 186 eyes 187 uh 188 know 189 right 190 stuff 191 sign 192 uh 193 takes 194 back 195 outside 196 pilot 197 know 198 homeroom 199 teacher 200 playing 201 hacky 202 sack 203 runway 204 uh 205 says 206 going 207 start 208 landing 209 important 210 part 211 pokes 212 ribs 213 says 214 know 215 fall 216 gets 217 sudden 218 stop 219 giggles 220 joke 221 funniest 222 joke 223 ever 224 heard 225 kicks 226 blue 227 plastic 228 milk 229 crate 230 foot 231 tall 232 tells 233 hop 234 tells 235 uh 236 jump 237 jump 238 feet 239 raise 240 two 241 little 242 kind 243 dust 244 clouds 245 like 246 neil 247 armstrong 248 gives 249 th 250 bs 251 like 252 got 253 right 254 stuff 255 says 256 going 257 going 258 teach 259 drop 260 roll 261 maneuver 262 land 263 basically 264 sounds 265 like 266 drop 267 roll 268 thing 269 would 270 fire 271 demonstrates 272 uh 273 like 274 know 275 romanian 276 gymnast 277 says 278 know 279 okay 280 try 281 uh 282 helps 283 back 284 onto 285 box 286 says 287 hop 288 bus 289 gus 290 think 291 nice 292 something 293 common 294 know 295 parents 296 grandparents 297 fans 298 uh 299 paul 300 simon 301 get 302 box 303 hold 304 onto 305 shoulder 306 steady 307 wanted 308 hold 309 onto 310 shoulder 311 say 312 rock 313 drop 314 like 315 stone 316 giggles 317 adorably 318 helps 319 picking 320 gravel 321 palms 322 hands 323 says 324 pretty 325 much 326 would 327 true 328 teaching 329 jump 330 milk 331 crate 332 could 333 gotten 334 diploma 335 gone 336 home 337 next 338 two 339 hours 340 uh 341 kind 342 gradually 343 raised 344 diving 345 platform 346 know 347 practice 348 platform 349 milk 350 crate 351 kitchen 352 chair 353 al 354 uh 355 kitchen 356 dinette 357 uh 358 table 359 painter 360 ladder 361 five 362 thousand 363 feet 364 sea 365 level 366 plane 367 uh 368 shows 369 canvas 370 strap 371 called 372 static 373 line 374 attached 375 plane 376 attached 377 chute 378 open 379 chute 380 automatically 381 jump 382 worry 383 anything 384 tugs 385 couple 386 times 387 demonstrate 388 withstand 389 full 390 upper 391 body 392 strength 393 teenage 394 girl 395 puts 396 one 397 hand 398 either 399 cheeks 400 leans 401 close 402 stares 403 know 404 fog 405 orange 406 goggles 407 wearing 408 says 409 listen 410 say 411 go 412 go 413 think 414 says 415 getting 416 cold 417 feet 418 know 419 need 420 little 421 help 422 embarrassed 423 everybody 424 know 425 gets 426 like 427 give 428 little 429 shove 430 says 431 worry 432 gentle 433 altitude 434 like 435 pillow 436 talk 437 basically 438 lovesick 439 know 440 trance 441 anything 442 shoot 443 think 444 makes 445 look 446 brave 447 uh 448 hop 449 onto 450 wing 451 look 452 like 453 pointy 454 steeples 455 pine 456 trees 457 get 458 little 459 cold 460 feet 461 second 462 thoughts 463 yells 464 go 465 uh 466 say 467 uh 468 uh 469 hear 470 actually 471 says 472 geronimo 473 gives 474 shove 475 gone 476 look 477 see 478 terrified 479 eyes 480 hear 481 say 482 wait 483 wait 484 chute 485 really 486 like 487 pathetic 488 inadequacy 489 two 490 hour 491 skydiving 492 lesson 493 hits 494 home 495 right 496 mean 497 took 498 longer 499 learn 500 knit 501 heading 502 powerlines 503 uh 504 like 505 reflex 506 know 507 reflexively 508 kind 509 squeezing 510 legs 511 uh 512 together 513 die 514 know 515 sliced 516 middle 517 like 518 couple 519 flounder 520 fillets 521 like 522 praying 523 life 524 flash 525 eyes 526 ruin 527 last 528 couple 529 minutes 530 suddenly 531 feel 532 jolt 533 yanked 534 around 535 big 536 white 537 fluffy 538 beautiful 539 canopy 540 opens 541 floating 542 earth 543 dead 544 silence 545 really 546 quiet 547 really 548 hear 549 heart 550 pounding 551 get 552 drag 553 gear 554 know 555 helmet 556 straps 557 chute 558 know 559 onion 560 field 561 back 562 office 563 annette 564 already 565 waiting 566 holding 567 plastic 568 cup 569 complimentary 570 sparkling 571 wine 572 talked 573 brochure 574 beaming 575 walk 576 say 577 fuck 578 know 579 that 580 giggles 581 says 582 know 583 seemed 584 like 585 cool 586 guy 587 wanted 588 really 589 awesome 590 jump 591 little 592 parachute 593 malfunction 594 thing 595 spike 596 adrenaline 597 last 598 minute 599 said 600 like 601 staring 602 trying 603 think 604 language 605 chick 606 might 607 know 608 understand 609 know 610 idea 611 dangerous 612 terrifying 613 illegal 614 probably 615 was 616 says 617 wait 618 wait 619 hey 620 right 621 late 622 meant 623 age 624 trying 625 think 626 know 627 going 628 knife 629 strangle 630 beat 631 death 632 heart 633 starts 634 kind 635 slowing 636 sort 637 starts 638 sinking 639 know 640 like 641 fell 642 mile 643 sky 644 purpose 645 without 646 chute 647 far 648 concerned 649 dropped 650 rolled 651 survived 652 think 653 little 654 joke 655 told 656 earlier 657 fall 658 gets 659 sudden 660 stop 661 think 662 know 663 maybe 664 right 665 going 666 stop 667 kills 668 lock 669 eyes 670 glare 671 hope 672 cute 673 angry 674 sign 675 another 676 lesson 677 drive 678 home 679 top 680 thank 681 (682, 100)\n"
     ]
    }
   ],
   "source": [
    "# here we read the text, run the reservoir - will do this twice for intact and scrambled\n",
    "\n",
    "#read in the narrative\n",
    "\n",
    "huge_list = []\n",
    "\n",
    "#with open(\"my_file_Gatsby_Kane_wolfe_bond.txt\", \"r\") as f:\n",
    "#    huge_list = f.read().split()::::\"iss_gatsby_long.txt\"\n",
    "with open(\"not-the-fall.txt\", \"r\", encoding=\"utf8\") as f:\n",
    "    huge_list = f.read().split()\n",
    "\n",
    "\n",
    "#print(huge_list)   \n",
    "\n",
    "discourse_words = huge_list\n",
    "\n",
    "# clean it up\n",
    "# large capital -> small capital\n",
    "\n",
    "discourse_words = [s.replace(s, s.lower()) for s in discourse_words]\n",
    "\n",
    "    # remove '.' and ',' from word list\n",
    "discourse_words = [s.replace('.', '') for s in discourse_words]\n",
    "discourse_words = [s.replace(',', '') for s in discourse_words]\n",
    "\n",
    "    # remove stop words from word list\n",
    "stop_words = stopwords.words('english')\n",
    "    #print(stop_words)\n",
    "for stop_word in stop_words:\n",
    "    while stop_word in discourse_words :\n",
    "        discourse_words.remove(stop_word)\n",
    "            \n",
    "            \n",
    "    # remove \"'s\" and \"'\" and \"-\" and \"'d\" and \"'ll\" and \"'ve\" and \"re\" from word list\n",
    "discourse_words = [s.replace(\"'s\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"'\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"-\", ' ') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"'d\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"'ll\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"'ve\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"'re\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"'m\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"(\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\")\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"[\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"]\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\",\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\";\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"-\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\":\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"?\", '') for s in discourse_words]\n",
    "discourse_words = [s.replace(\"!\", '') for s in discourse_words]\n",
    "\n",
    "\n",
    "\n",
    "#print(discourse_words)\n",
    "\n",
    "inputDataTesting = np.empty((0,vectorDim))\n",
    "print(inputDataTesting.shape)\n",
    "\n",
    "for num in range(len(discourse_words)):\n",
    "    inputDataTesting = np.append(inputDataTesting, np.array([wiki2vec.get_word_vector(discourse_words[num])]), axis=0)\n",
    "    print(discourse_words[num], num, end = ' ')\n",
    "print(inputDataTesting.shape)\n",
    "\n",
    "# set the seed\n",
    "np.random.seed(res_seed)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% (9861 of 9861) |####################| Elapsed Time: 0:00:05 Time:  0:00:05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 1101)\n",
      "(1500, 1000)\n",
      "(1500, 100)\n",
      "1000\n"
     ]
    }
   ],
   "source": [
    "# here we create an input vector that is mainly zeros, and has a few values, to \"ping\" the reservoir\n",
    "#start with just one\n",
    "\n",
    "inputImpulseTesting = np.zeros((1500,vectorDim))\n",
    "\n",
    "for i in range(400):\n",
    "    #inputImpulseTesting[i] = inputDataTesting[110]\n",
    "    inputImpulseTesting[i+500] = inputDataTesting[200]\n",
    "\n",
    "\n",
    "# set the seed\n",
    "np.random.seed(res_seed)\n",
    "\n",
    "# set up the reservoir\n",
    "# leakingRate=0.2 changing too fast, mutliple with 0.05; changed\n",
    "esn = PredictionESN(n_input=vectorDim, n_output=vectorDim, n_reservoir=numNode, leakingRate=0.15, regressionParameters=[1e-2], solver=\"lsqr\", feedback=False)\n",
    "\n",
    "# train the reervoir with a new seed\n",
    "esn.fit(in_small, out_small, transientTime=\"Auto\", verbose=1)\n",
    "\n",
    "#run the reservoir\n",
    "reservoirStatesBuffer = np.empty((0,numNode))\n",
    "prediction,reservoirStatesBuffer  = esn.predict(inputImpulseTesting)\n",
    "reservoirStatesBuffer = reservoirStatesBuffer.T\n",
    "print(reservoirStatesBuffer.shape)\n",
    "reservoirStatesBuffer = reservoirStatesBuffer[:,101:]\n",
    "print(reservoirStatesBuffer.shape)\n",
    "print(prediction.shape)\n",
    "\n",
    "# set the similarity matrix\n",
    "sim_matrix1 = np.empty([len(reservoirStatesBuffer),len(reservoirStatesBuffer)])\n",
    "       \n",
    "print(len(reservoirStatesBuffer[1]))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "scale = 0.999\n",
    "\n",
    "fig= plt.figure(figsize=(16,10))\n",
    "\n",
    "plt.subplot(122)\n",
    "#plt.imshow(np.corrcoef(sim_matrix1[280:680,280:680]))\n",
    "plt.imshow(np.corrcoef(reservoirStatesBuffer), vmin=scale, vmax=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('reservoir-ping.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resn = 100\n",
    "begin=0\n",
    "end=1500\n",
    "fig= plt.figure(figsize=(16,4))\n",
    "\n",
    "plt.plot( reservoirStatesBuffer[begin:end,4:5] )\n",
    "plt.title('Reservoir Temporal Structure Intact vs Shifted')\n",
    "#plt.axvline(x=299, color='b', linestyle='--')\n",
    "#plt.axvline(x=300, color='b', linestyle='--')\n",
    "plt.axvline(x=500, color='b', linestyle='--')\n",
    "plt.axvline(x=900, color='b', linestyle='--')\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "#plt.savefig('reservoirstates--not-the-fall-differences1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resn = 10\n",
    "\n",
    "fig= plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.plot( reservoirStatesBuffer[:,:resn] )\n",
    "plt.title('Reservoir response to fixed input and zero')\n",
    "#plt.axvline(x=299, color='b', linestyle='--')\n",
    "#plt.axvline(x=300, color='b', linestyle='--')\n",
    "plt.axvline(x=500, color='b', linestyle='--')\n",
    "plt.axvline(x=900, color='b', linestyle='--')\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig('reservoirstates-IMPULSE-1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(200, 1000)\n",
      "(1000, 200)\n",
      "1000\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "# prepare data for calculating time contant based on ping\n",
    "resn = numNode\n",
    "index = 0\n",
    "begin=500\n",
    "end=700\n",
    "data = reservoirStatesBuffer[begin:end,index:index+resn]\n",
    "print(data.shape)\n",
    "dataT=data.T\n",
    "print(dataT.shape)\n",
    "print(dataT.shape[0])\n",
    "print(dataT.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 200)\n"
     ]
    }
   ],
   "source": [
    "#since the neurons do not all  stablize at zero, need to shift related to their final stable value\n",
    "shiftedData = dataT\n",
    "print(dataT.shape)\n",
    "for neuron in range(dataT.shape[0]):\n",
    "        for timestep in range(dataT.shape[1]):\n",
    "            shiftedData[neuron,timestep] = dataT[neuron,timestep] - dataT[neuron,dataT.shape[1]-1]\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  9.   6.   6.   7.   3.   2.  23.   5.   4.   5.   5.   5.   5.  20.\n",
      "  18.   5.   3.  16.   3.   5.  11.  23.  20.   5.   4.   5.   3.   3.\n",
      "   7.  27.   5.   2.  12.   4.   8.  17.   6.  10.   5.   3.  23.  13.\n",
      "   3.   5.   6.   1.   3.   9.  10.   5.  11.   5.   4.   5.   4.   5.\n",
      "   5.  15.  37.   6.   4.   9.   7.   8.   5.   4.  16.   9.   3.   6.\n",
      "   5.   2.   2.   3.  10.   9.  13.   5.   3.  25.  37.   5.   6.  10.\n",
      "   4.   3.  24.  40.   5.  30.   5.   9.  12.   8.   7.  33.   2.   5.\n",
      "   1.  25.   5.   6.   5.  29.   5.  42.   8.   4.  19.   8.   5.  33.\n",
      "   2.   5.  17.   1.   3.   5.   4.   5.   6.   5.   5.   4.  42.  14.\n",
      "   6.   5.  14.   6.   6.   9.   3.   7.  12.   5.   4.   4.   5.   5.\n",
      "  31.   6.   5.   4.  17.   4.   5.   2.   3.  10.   1.  13.   5.   6.\n",
      "   5.  12.   5.  29.   1.  15.   8.  10.  20.   5.  51.   4.   5.  13.\n",
      "   5.   5.   2.  10.   4.  11.  13.   5.  18.  17.   5.   4.  10.   5.\n",
      "   1.   5.   8.  15.  23.   5.   5.   6.   4.   3.   6.   2.  12.   6.\n",
      "  63.  12.  21.  11.   5.   5.  19.   5.  13.  18.   4.   5.   7.   6.\n",
      "   2.   5.   5.  16.   8.  13.   2.   2.   2.   4.  28.   7.   4.   7.\n",
      "   3.   6.   1.  10.   5.   1.   6.   3.   5.   6.   5.   2.  16.   5.\n",
      "   5.  25.   1.   1.   6.   3.   5.   5.  11.   4.   3.   2.   2.  11.\n",
      "   1.  27.   6.   8.   4.  19.  29.   5.   5.  22.   1.   6.   5.  38.\n",
      "   5.  43.   2.   8.  20.   6.   2.  15.   6.  28.   8.   2.   5.   5.\n",
      "   4.  12.  33.  12.   4.   5.   7.  11.   8.   6.   3.   5.  18.   3.\n",
      "   5.   2.   5.   4.   5.   5.   6.  33.  67.  10.   6.   6.   6.   5.\n",
      "  29.   5.   9.   9.   4.   7.  29.   1.  19.   7.   3.  17.   7.   6.\n",
      "   8.   4.   6.   5.   5.   8.  10.   5.   5.   6.  76.   7.  12.  19.\n",
      "   6.  32.  10.   6.  11.   6.  11.  34.   2.   4.  11.   6.  17.  17.\n",
      "  18.   5.   5.  11.   5.   5.   3.   3.  16.   2.  10.   1.  11.   7.\n",
      "   5.   2.   3.   4.   5.   4.   9.   4.   7.  12.  55. 112.   3.   5.\n",
      "   3.  12.   8.   3.   6.   5.   4.   4.  25.   4.   6.   5.   2.   4.\n",
      "  21.  18.   3.  21.   4.   8.  13.   6.   6.   6.   4.  11.   6.  13.\n",
      "   4.   4.   5.   9.   3.   7.   5.   7.  10.   6.   6.  60.   8.   3.\n",
      "   4.   4.  46.   6.  11.   5.  12.  13.   6.  11.   5.   4.  29.   7.\n",
      "   5.   3.   8.   8.  16.   5.  25.   2.   2.  19.   6.   7.   5.   6.\n",
      "   3.   5.  13.   5.   2.  33.  31.   6.   5.  22.   5.   2.  13.  42.\n",
      "   6.  31.   4.   8.   6.   9.   8.   5.  22.   4.  11.  10.   5.   6.\n",
      "   8.  13.   5.   5.   6.   4.  21.   5.   5.   7.   3.   5.   5.   9.\n",
      "   6.   5.   3.   5.   7.   5.   8.   5.   5.   8.   6.   6.   4.   3.\n",
      "   5.   4.   2.   6.   1.   7.   5.   5.   5.  31.  16.   5.   5.   6.\n",
      "   2.  36.   6.  12.   4.   6.   3.  29.   2.  20.   2.  12.   8.   5.\n",
      "   5.   7.  10.  19.   7.   4.   9.   7.  24.   5.  27.   8.   5.   8.\n",
      "   8.   5.  34.  31.   5.  30.  44.   5.   5.  28.  16.  11.  17.  15.\n",
      "   5.   5.   5.   3.  32.   9.   9.   4.  12.   3.   2.   2.  19.   6.\n",
      "  45.   5.  16.   9.   4.  53.  32.   8.   8.   6.   5.  16.   3.   3.\n",
      "   6.   3.   3.   5.  11.   4.   5.   5.   3.  11.   5.   6.   7.  20.\n",
      "  15.   8.   4.   5.   3.   5.  13.   4.   9.   2.   2.   3.   9.  18.\n",
      "   5.  14.   2.   2.   5.   6.  34.   5.   5.  43.  25.   4.   4.   8.\n",
      "  12.   4.   3.  17.  21.   3.   4.   5.   5.   3.   4.   2.  25.   5.\n",
      "   2.   9.   6.   5.   6.  13.  11.   4.   3.   4.   2.   4.  14.  17.\n",
      "   8.   5.   5.   4.   3.  23.   4.  11.  21.  13.  22.   8.   3.  22.\n",
      "   6.   3.   1.  21.   6.  20.   7.   5.   7.   5. 164.   6.   2.   5.\n",
      "  10.   5.   7.   3.   5.  31.   6.   4.   5.  22.   6.  39.  25.   4.\n",
      "   7.  40.   6.   4.  21.   1.   4.  11.   3.   5.   6.  27.   6.   8.\n",
      "   2.  12.   5.   4.   5.   4.   5.  12.   9.   6.   2.   7.  11.  12.\n",
      "  10.   5.  11.   4.  23.  29.  21.  12.  24.   4.   5.   5.   8.  16.\n",
      "   6.   3.  13.   6.   6.   2.  22.   6.   7.   8.  37.   3.   8.  19.\n",
      "   4.   7.   5.  10.  12.   6.  82.   4.  20.   4.   6.  14.   8.   5.\n",
      "   5.   1.   4.  21.   5.   1.  13.  11.   5.   7.  11.   8.  17.   4.\n",
      "   7.  16.   5.  15.   4.   5.   1.   6.   8.   4.   6.   2.  21.   4.\n",
      "   7.   8.   4.   4.  71.  46.   3.  23.   3.   5.  15.   3.   2.   7.\n",
      "   3.   6.   3.   5.   3.   1.  17.   5.   3.   5.   5.  13.   4.  24.\n",
      "   5.   5.  28.   3.   7.   6.  12.   7.   7.  19.   1.   6.   5.  15.\n",
      "   5.   8.   7.   7.  11.   3.  19.   5.   6.   8.   1.   3.   2.  20.\n",
      "  11.   5.   5.   7.  23.   8.  20.  16.   2.   5.   5.   3.   2.  13.\n",
      "   6.   9.   5.   4.  19.   5.  15.   5.   4.   6.   3.   6.   5.   2.\n",
      "   5.  23.   5.  12.   3.  17.  11.  15.  14.  16.  13.  13.   4.   5.\n",
      "   7.   6.  23.   7.  17.  18.   1.  12.  26.   4.  27.   5.   1.   7.\n",
      "   5.   5.   8.   5.   5.   6.   4.   5.  22.   2.   5.   5.   7.   5.\n",
      "   5.   6.   5.   9.  17.  19.   4.   3.  15.   6.   5.   6.   3.   6.\n",
      "  13.   9.   5.  11.  10.   3.   5.   9.  57.   5.   2.   8.  12.   4.\n",
      "   5.  22.   7.  30.   3.   1.   8.   1.   9.   1.   6.  19.  18.   9.\n",
      "  41.  13.   5.   9.   7.   2.   3.   5.   7.   5.   2.   7.   3.   6.\n",
      "  22.   2.  19.   4.  16.   5.   8.  38.   3.   7.  23.   5.  14.   5.\n",
      "   5.   9.   6.   8.  16.   5.]\n",
      "1000\n",
      "[7.02081127e-01 1.15689390e+00 6.28710455e-01 5.87984001e-01\n",
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      " 7.84843555e-01 1.79389917e-01 8.19488584e-02 1.11644927e+00\n",
      " 8.72910005e-01 1.39893006e-03 1.35049061e-02 1.05747505e-01\n",
      " 1.02265164e+00 9.48481321e-01 8.79948784e-01 5.21165975e-01\n",
      " 4.69622340e-01 2.90355449e-01 3.11292853e-01 3.76164794e-01\n",
      " 3.70346694e-01 1.16549999e+00 4.35534534e-01 2.66668461e-01\n",
      " 6.07750207e-01 3.34957235e-01 2.53166783e-01 6.95124142e-01\n",
      " 1.26559451e-02 9.48272728e-01 7.22101281e-01 4.05889613e-01\n",
      " 8.31998179e-01 1.27135814e+00 5.45249109e-01 1.24889177e-01\n",
      " 5.18444894e-01 2.00896067e-02 1.93834178e-01 7.37321370e-02\n",
      " 3.39900885e-01 8.26095779e-01 6.40057249e-01 1.17413818e-01\n",
      " 3.51263790e-01 3.20988108e-01 8.50905626e-02 2.70714445e-01\n",
      " 5.58041706e-01 2.22696549e-01 5.62559620e-01 6.36522516e-01\n",
      " 3.52726889e-01 4.48182926e-01 3.94278868e-01 3.10175011e-01\n",
      " 1.09958748e+00 5.43115519e-01 4.87404803e-02 7.69882132e-02\n",
      " 6.86843140e-01 3.76390698e-01 7.33958182e-01 1.97845080e-01\n",
      " 7.04396034e-01 2.67132862e-01 2.06510924e-04 1.38374516e+00\n",
      " 4.54528038e-01 3.12559337e-01 3.96478776e-01 6.68178440e-01\n",
      " 7.12568161e-01 5.38395953e-01 9.69944997e-01 1.46681069e-01\n",
      " 1.34121077e+00 7.45519957e-01 4.02493530e-01 8.52006688e-02\n",
      " 1.12990493e+00 4.27842269e-02 5.03972498e-01 5.59015658e-01\n",
      " 5.54458160e-01 3.53661179e-01 1.12785947e+00 6.14984765e-01\n",
      " 1.38916776e-01 2.60305234e-02 5.81401951e-01 6.53963146e-01\n",
      " 4.91582148e-01 3.29107941e-01 4.31723698e-01 9.69547880e-02\n",
      " 8.47557527e-01 1.00379247e+00 2.99092572e-01 9.44745962e-01\n",
      " 7.97663397e-01 3.62746606e-01 7.45436640e-01 3.48583431e-01\n",
      " 9.66564469e-01 8.18501258e-01 5.31699803e-01 4.87688036e-01\n",
      " 2.96785730e-01 6.68692813e-01 8.89973775e-01 2.45024012e-01\n",
      " 1.47807387e+00 6.95323390e-01 7.68932114e-01 1.97364793e-01\n",
      " 2.90978967e-01 1.98730942e-01 3.10636439e-01 9.46698855e-01\n",
      " 2.29493762e-01 5.02250420e-01 5.04253765e-01 2.99471773e-01\n",
      " 2.65529885e-01 2.98992598e-01 1.18035077e+00 3.23536565e-01\n",
      " 1.00698278e+00 5.22584311e-01 4.29598564e-01 3.35792414e-01\n",
      " 1.16141021e-01 1.72866788e-01 4.00056437e-01 2.73612677e-01\n",
      " 1.60364380e-01 6.56362842e-02 6.22649766e-01 3.37379700e-02\n",
      " 5.28228825e-01 5.79961791e-01 5.13951690e-01 4.93038206e-01\n",
      " 1.71614814e-01 8.29257928e-01 2.99884183e-03 1.26218929e-01\n",
      " 6.45579450e-01 6.87548923e-01 5.88138825e-01 1.67773386e-01\n",
      " 8.79243874e-01 7.67089340e-01 1.85830924e-01 1.16086254e-01\n",
      " 7.41611869e-01 7.61893450e-03 6.67394094e-01 1.00027888e-01\n",
      " 6.65066503e-01 7.62546581e-01 5.17870716e-01 1.40170153e-01\n",
      " 7.17010334e-01 3.16023596e-01 3.97632122e-01 5.23096729e-01\n",
      " 1.13762687e+00 6.77170193e-01 1.28989429e-01 5.19799206e-01\n",
      " 9.92780909e-01 5.61323395e-01 4.26911264e-02 6.30760946e-01\n",
      " 1.06268014e+00 2.75887895e-01 4.93208858e-01 2.01328561e-01\n",
      " 3.11052414e-01 2.78877015e-01 8.19198843e-01 9.33331384e-01\n",
      " 2.79830106e-01 1.64789860e-01 8.32080222e-03 5.34513647e-02\n",
      " 1.38603086e-01 2.08807985e-01 4.51907066e-01 5.96386245e-01\n",
      " 1.55045077e-01 2.32119479e-01 3.93233856e-01 1.49146642e-01\n",
      " 2.75210150e-01 3.25954136e-01 2.10118324e-01 5.62031271e-01\n",
      " 2.14097151e-01 1.56083722e-01 4.73978221e-02 5.46349056e-01\n",
      " 2.28018645e-01 9.68485860e-01 7.74662725e-01 4.21435117e-01\n",
      " 6.08329247e-01 3.37691200e-01 5.59553899e-01 4.86541794e-01\n",
      " 1.34607717e-01 1.71804303e-01 8.22423804e-01 1.16145903e+00\n",
      " 6.88398546e-01 5.86490044e-01 9.55115358e-01 5.16650588e-01\n",
      " 1.13719954e-01 4.06884310e-01 1.49110916e-01 5.04070930e-01\n",
      " 8.71791239e-01 1.58456758e+00 7.18716363e-01 4.76359355e-01\n",
      " 2.87164737e-01 2.14340948e-01 3.21692140e-01 4.04460338e-01\n",
      " 2.02210260e-01 5.10124741e-01 1.12649369e-01 4.15630864e-01\n",
      " 3.12253286e-01 1.33655016e-01 5.32929648e-01 1.34055607e+00\n",
      " 8.60545592e-01 5.81376609e-01 3.05723385e-01 8.65859383e-01\n",
      " 2.27875628e-01 3.84517821e-01 8.10116421e-02 7.84378786e-01\n",
      " 7.14823932e-01 3.44168088e-01 1.56142507e-01 3.54513171e-01\n",
      " 5.11592939e-01 1.01874204e+00 4.28224884e-01 5.94704193e-01\n",
      " 7.37022882e-01 6.13534039e-01 1.22090806e-01 9.17876550e-01\n",
      " 5.26562100e-01 6.58812526e-01 5.51291663e-01 9.34444516e-01\n",
      " 8.07245934e-01 5.61204831e-01 5.55600055e-01 5.15798650e-01\n",
      " 1.63571183e-01 1.23047320e+00 2.07377728e-01 7.73737735e-01\n",
      " 5.95815215e-01 5.13238789e-01 7.39960881e-01 3.13735064e-01\n",
      " 2.75455916e-01 1.35097230e-01 8.22125902e-01 4.74673182e-01\n",
      " 1.01367136e+00 1.88294711e-02 4.15597189e-02 2.46375449e-01\n",
      " 2.52839031e-01 2.59553887e-01 4.06174269e-02 2.88877306e-01\n",
      " 3.20520160e-01 2.92691944e-01 2.75399269e-01 8.58371192e-01\n",
      " 5.66584325e-02 7.77212180e-01 3.13614321e-01 2.76695393e-01\n",
      " 4.72010617e-01 5.26612670e-01 4.39917244e-01 3.57396162e-01\n",
      " 4.74849426e-01 1.10604168e+00 2.94366443e-01 8.21332079e-02\n",
      " 1.77759657e-01 2.68495533e-01 1.24729179e+00 3.91953411e-01\n",
      " 1.59457958e-01 1.06892334e+00 1.29061671e+00 2.68071487e-01\n",
      " 8.82775968e-01 4.60604168e-01 8.22490047e-01 1.60148657e-01\n",
      " 4.33841044e-01 1.39364856e+00 8.16130881e-01 4.01931059e-01\n",
      " 6.07944351e-02 1.19183604e+00 2.53188568e-01 1.82641786e-01\n",
      " 4.33604025e-01 6.73887151e-01 1.06675683e+00 2.40413275e-01\n",
      " 9.47182164e-01 1.04635128e-01 3.27077758e-02 6.80032415e-01\n",
      " 1.78909466e-01 7.33601001e-01 4.50992525e-01 4.05673737e-01\n",
      " 1.01132236e+00 4.90753043e-01 9.94162029e-01 3.96877540e-02\n",
      " 3.43715112e-01 6.11718688e-02 1.15629753e+00 1.26655402e-01\n",
      " 9.23165616e-01 1.64861103e-01 8.73143672e-01 5.23235832e-01\n",
      " 1.47936322e-01 6.22930562e-02 1.35774808e-01 4.87861126e-01\n",
      " 8.93907812e-01 1.63287698e-01 8.87105658e-01 1.42850001e-01\n",
      " 6.28405980e-02 3.59008926e-01 3.04246858e-01 2.96027565e-01\n",
      " 1.72301022e-01 1.05046723e+00 1.28305943e-01 4.29257554e-01\n",
      " 3.67192262e-01 8.84203085e-02 4.09033404e-01 4.80173017e-01\n",
      " 4.67679152e-01 4.12620427e-01 1.16873400e+00 2.71259484e-02\n",
      " 3.35457293e-01 2.20974702e-01 2.69271051e-01 4.57059026e-01\n",
      " 8.23801437e-01 3.69817942e-01 1.03934818e+00 1.03357596e-01\n",
      " 1.13115675e+00 5.63702205e-01 2.48351433e-01 2.71958844e-01]\n"
     ]
    }
   ],
   "source": [
    "timeconstants = np.zeros(shiftedData.shape[0])\n",
    "res_maxes = np.zeros(shiftedData.shape[0])\n",
    "\n",
    "#calculate the alignment time:  shiftedData(neurons,time)\n",
    "#interate over reservoir neuron\n",
    "for neuron in range(shiftedData.shape[0]):\n",
    "    # get the value\n",
    "    max = abs(shiftedData[neuron,0])\n",
    "    if max > -1:  #WAS 0.05\n",
    "        for timestep in range(shiftedData.shape[1]):\n",
    "            if (abs(shiftedData[neuron,timestep]) < max/2):\n",
    "                timeconstants[neuron] = timestep\n",
    "                res_maxes[neuron] = max\n",
    "                max=-1     \n",
    "                \n",
    "print(timeconstants)\n",
    "print(len(timeconstants))\n",
    "print(res_maxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_timeconstants = timeconstants\n",
    "timeconstants = np.sort(save_timeconstants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(15,4))\n",
    "#488\n",
    "begin = 0  #166\n",
    "end = 800    #468\n",
    "gs = gridspec.GridSpec(1, 2,width_ratios=[3,1])\n",
    "plt.subplot(gs[0])\n",
    "plt.plot(timeconstants[begin:end])\n",
    "#plt.axhline(y=13, color='b', linestyle='--')\n",
    "\n",
    "plt.title('Temporal profiles of context construction alignment times', fontsize=16)\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(gs[1])\n",
    "bp = ax.boxplot(timeconstants[begin:end], showmeans=True)\n",
    "#plt.hist(timeconstants[begin:end], bins = 10)\n",
    "\n",
    "plt.title('Alignment times', fontsize=16)\n",
    "#plt.axhline(y=13, color='b', linestyle='--')\n",
    "\n",
    "plt.savefig('not-the-fall-impulse-900-1100.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 1000)\n",
      "1500\n",
      "1000\n",
      "1000\n"
     ]
    }
   ],
   "source": [
    "reservoirStatesBuffer1 = reservoirStatesBuffer\n",
    "print(reservoirStatesBuffer1.shape)\n",
    "#that is (timesteps, neuron)\n",
    "timesteps = reservoirStatesBuffer1.shape[0]\n",
    "neurons = reservoirStatesBuffer1.shape[1]\n",
    "print(timesteps)\n",
    "print(neurons)\n",
    "print(len(timeconstants))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a column to store the time constants into the \n",
    "\n",
    "reservoirStates1SortedT = np.zeros((neurons,timesteps+1))\n",
    "reservoirStates1SortedT[:,:-1] = reservoirStatesBuffer1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "reservoirStates1SortedT[:,682]=save_timeconstants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "SortedStates = reservoirStates1SortedT[np.argsort(reservoirStates1SortedT[:, 682])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1501)\n",
      "(1501, 1000)\n",
      "(1501, 50)\n"
     ]
    }
   ],
   "source": [
    "# now we can create the pattern correlation matrices for different time-constant plages\n",
    "print(SortedStates.shape)\n",
    "SortedStatesT=SortedStates.T\n",
    "print(SortedStatesT.shape)\n",
    "print(SortedStatesT[:,400:450].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 1501)\n",
      "(1501, 1000)\n",
      "(1501, 50)\n"
     ]
    }
   ],
   "source": [
    "# now we can create the pattern correlation matrices for different time-constant plages\n",
    "print(SortedStates.shape)\n",
    "SortedStatesT=SortedStates.T\n",
    "print(SortedStatesT.shape)\n",
    "print(SortedStatesT[:,400:450].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(save_timeconstants,res_maxes)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}