Fermi_GBM.ipynb 212 KB
Newer Older
toastmaker's avatar
toastmaker committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from astropy.io import fits\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glg_ctime_b0_bn200224416_v00.pha    glg_healpix_all_bn200224416_v01.fit\r\n",
      "glg_ctime_b0_bn200224416_v02.rsp    glg_trigdat_all_bn200224416_v01.fit\r\n",
      "glg_ctime_b0_bn200224416_v02.rsp2\r\n"
     ]
    }
   ],
   "source": [
    "!ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: data/glg_ctime_b0_bn200224416_v00.pha\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU      37   ()      \n",
      "  1  EBOUNDS       1 BinTableHDU     58   8R x 3C   [1I, 1E, 1E]   \n",
      "  2  SPECTRUM      1 BinTableHDU     72   14824R x 5C   [8I, 1E, 1I, 1D, 1D]   \n",
      "  3  GTI           1 BinTableHDU     44   1R x 2C   [1D, 1D]   \n"
     ]
    }
   ],
   "source": [
    "#from https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/current/\n",
    "fname=\"data/glg_healpix_all_bn200224416_v01.fit\"\n",
    "fname=\"data/glg_trigdat_all_bn200224416_v01.fit\"\n",
    "fname=\"data/glg_ctime_b0_bn200224416_v00.pha\"\n",
    "fits.info(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from time-resolved (8-bin) spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/current/glg_ctime_n5_bn200224416_v00.pha [Done]\n"
     ]
    }
   ],
   "source": [
    "fname=\"https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn%s/current/glg_ctime_n%i_bn%s_v00.pha\" #NaI det spectra\n",
    "fname2=\"https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn%s/current/glg_ctime_b%i_bn%s_v00.pha\" #BGO det\n",
    "bid=\"200412381\"\n",
    "bid=\"200224416\"\n",
    "ndet=5 #from 0 to 11\n",
    "dd=fits.getdata(fname%(bid,ndet,bid),ext=2)\n",
    "times=np.array([d[3] for d in dd])\n",
    "allcurves=np.array([d[0] for d in dd])\n",
    "expo=times[1:]-times[:-1] #time of integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/current/glg_ctime_n0_bn200224416_v00.pha [Done]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([26, 78, 46, 40, 50,  6,  9, 22], dtype=uint16), 0.25512588, 0, 604230130.81251, 604230131.068528)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#bid='200224416'\n",
    "dd2=fits.getdata(fname%(bid,0,bid),ext=2)\n",
    "times2=np.array([d[3] for d in dd2]) #same as times\n",
    "allcurves2=np.array([d[0] for d in dd2])\n",
    "dd2[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \n",
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in true_divide\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa683899198>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "t0=times[0]\n",
    "tmax=times[np.argmax(allcurves[:-1,4]/expo)]-t0\n",
    "plt.plot(times[:-1]-t0,allcurves[:-1,3]/expo) #energy channel 4\n",
    "plt.plot(times[:-1]-t0,allcurves[:-1,4]/expo) #energy channel 5\n",
    "#plt.xlim(tmax-20,tmax+20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in true_divide\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa683b679e8>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#t0=times[0]\n",
    "#tmax=times[np.argmax(allcurves[:-1,4]/expo)]-t0\n",
    "plt.plot(times[:-1]-t0,allcurves2[:-1,3]/expo)\n",
    "plt.plot(times[:-1]-t0,allcurves2[:-1,4]/expo)\n",
    "#plt.xlim(tmax-20,tmax+20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in true_divide\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa6837a0c18>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sel1=abs(times[:-1]-t0-tmax)<40\n",
    "exsel=expo[sel1]\n",
    "emin=3\n",
    "c1,c2=allcurves[:-1,emin][sel1]/exsel,allcurves2[:-1,emin][sel1]/exsel\n",
    "c1-=c1[-100:].mean()\n",
    "c2-=c2[-100:].mean()\n",
    "mcurv=np.correlate(c1,c2,\"same\")\n",
    "plt.plot(mcurv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from event list - user defined binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname3=\"https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn%s/current/glg_tte_n%i_bn%s_v00.fit\"\n",
    "elist=fits.getdata(fname3%(bid,5,bid),ext=2)\n",
    "elist2=fits.getdata(fname3%(bid,3,bid),ext=2)\n",
    "#elist\n",
    "np.savez(\"tdata.npz\",[elist,elist2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pack=np.load(\"tdata.npz\",allow_pickle=True)\n",
    "elist,elist2=pack['arr_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lcurve(time,fac=600,sub=0):\n",
    "    #fac: grouping factor - binning = 1/fac seconds\n",
    "    tlab=(time*fac).astype(int)\n",
    "    pos=np.r_[0,np.where(tlab[1:]-tlab[:-1])[0]]\n",
    "    cnt=pos[1:]-pos[:-1]\n",
    "    if sub!=0: #baseline\n",
    "        basel=np.median(cnt[sub:])\n",
    "        cnt=cnt.astype(float)-basel\n",
    "    return np.r_[tlab[0]:tlab[0]+len(cnt)]/fac,cnt*fac\n",
    "\n",
    "t0=elist['TIME'][0]\n",
    "tevs=elist['TIME']-t0\n",
    "t,r=lcurve(tevs,50)\n",
    "#pl.plot(t,r)\n",
    "tmax=t[np.argmax(r)]\n",
    "sel2=abs(tevs-tmax)<40\n",
    "evts=elist['PHA'][sel2]\n",
    "\n",
    "tevs2=elist2['TIME']-t0\n",
    "sel22=abs(tevs2-tmax)<40\n",
    "evts2=elist2['PHA'][sel22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "bin1,cur1=lcurve(tevs[sel2],100,-200)\n",
    "plt.plot(bin1,cur1)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": [
    "bin2,cur2=lcurve(tevs2[sel22],100,-200)\n",
    "plt.plot(bin2,cur2)\n",
    "plt.grid()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAD8CAYAAABdCyJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJztnXmYFcW1wH+HYZd9VxZBAREUUSe4xC2iCPoiJmpCEiNJNCSuSTQvYvSpcUkwy/MlcYtR4xIjEqKRCIq4LygwqCCIyIAsI8giCCKyzZz3x+07c+dOd9/u23234fy+b77pW7eq+nTd6jq1nDolqophGIZhZEuTQgtgGIZhlDamSAzDMIxImCIxDMMwImGKxDAMw4iEKRLDMAwjEqZIDMMwjEiYIjEMwzAiYYrEMAzDiIQpEsMwDCMSTePIRERGAX8EyoB7VXVi2vctgIeAI4FPgG+q6grnu6uBC4Bq4HJVneGE3w/8F7BeVQ9JyasT8BjQF1gBfENVN/vJ16VLF+3bt2/UxzQMw9irmDdv3kZV7ZopnkR1kSIiZcAHwKlAFTAX+JaqvpcS52JgqKr+WETGAl9T1W+KyGDgUWA4sB/wHDBQVatF5ARgG/BQmiL5LbBJVSeKyASgo6pe5SdjeXm5VlRURHpOwzCMvQ0Rmaeq5ZnixTG1NRyoVNXlqroLmASMSYszBnjQuZ4CjBARccInqepOVf0QqHTyQ1VfATa53C81rweBs2J4BsMwDCNL4lAkPYHVKZ+rnDDXOKq6B9gCdA6YNp3uqrrWyWst0C1ryQ3DMIzIxKFIxCUsfb7MK06QtFkhIuNFpEJEKjZs2BBHloZhGIYLcSiSKqB3yudewBqvOCLSFGhPYtoqSNp01onIvk5e+wLr3SKp6j2qWq6q5V27ZlwrMgzDMLIkDkUyFxggIv1EpDkwFpiaFmcqMM65Pgd4QROr/FOBsSLSQkT6AQOAORnul5rXOODJGJ7BMAzDyJLIisRZ87gUmAEsBiar6iIRuVFEznSi3Qd0FpFK4ApggpN2ETAZeA94BrhEVasBRORR4A3gIBGpEpELnLwmAqeKyFISlmL1TI0NwzCM/BLZ/LcUMPNfwzCM8OTT/NfIM88u+pj1W3cUWgzDMAzAFEnJsae6hvEPz2PsPW8WWhTDMAzAFEnJkZyIXLVpe0HlMAzDSGKKxDAMw4iEKRLDMAwjEqZISoy9wMjOMIwSwxSJYRiGEQlTJCWGuHknMwzDKCCmSEoMm9oyDKPYMEVSotjIxDCMYsEUiWEYhhEJUySGYRiNhAVVn/J65ca837dp3u9oGIZh5IQzb38dgBUTz8jrfW1EUkA+3b6LT7btLLQYhmEYkbARSQEZduNMIP+9B8MwjDiJZUQiIqNEZImIVIrIBJfvW4jIY873s0Wkb8p3VzvhS0TktEx5isgIEXlLRN4RkddEpH8cz2AYhmFkR2RFIiJlwB3AaGAw8C0RGZwW7QJgs6r2B24DbnXSDiZxNO8QYBRwp4iUZcjzLuA7qjoM+AdwbdRnKCUU20hiGEZxEceIZDhQqarLVXUXMAkYkxZnDPCgcz0FGCEi4oRPUtWdqvohUOnk55enAu2c6/bAmhieoeQQbCOJYRjFQRxrJD2B1Smfq4CjvOKo6h4R2QJ0dsLfTEvb07n2yvNCYLqIfAFsBY6O4RlKDhuZGIZRLMQxInHrGqe3cl5xwoYD/Aw4XVV7AX8D/tdVKJHxIlIhIhUbNmxwFdwobmpqlEv+8RZvrdpcaFEMw/AhDkVSBfRO+dyLhtNNtXFEpCmJKalNPmldw0WkK3CYqs52wh8DjnUTSlXvUdVyVS3v2rVrNs9VlOxNvrbWf7aTaQvWctHf5xVaFKORsn3XHv78/FL2VNcUWpSSJg5FMhcYICL9RKQ5icXzqWlxpgLjnOtzgBdUVZ3wsY5VVz9gADDHJ8/NQHsRGejkdSqwOIZnKDlsjcQwovN/zy3lDzM/4PG3Pyq0KCVN5DUSZ83jUmAGUAbcr6qLRORGoEJVpwL3AQ+LSCWJkchYJ+0iEZkMvAfsAS5R1WoAtzyd8B8C/xKRGhKK5QdRn8EwjL2Tz3fuAWDn7uoCS9IQVeXN5Zs4+oBOSJF7aY1lQ6KqTgemp4Vdl3K9AzjXI+0twC1B8nTCnwCeiCiyYeSFG6Yu4qh+nRh96L6FFsUoMf6zYC2XP/o2E79+KGOH9ym0OL6YixSjaGkMlmkPzFrBRY+8VWgxDA+SHf1c1bSaGmX5hm1ZpV29aTsAK53/xYwpEqPosfUgI1fkum7d9fIyTv7Dy7y3ZmtO71NoTJEYRU9jGJkYxU2urCHfWpkwXf/o0y+yzqMULDVNkZQYpVCp4sJGIkauyfUadq7zX/XJdnbtKbzpsimSUsXaWMMoGTRCD9BLGW3ZvpsTfvci1/773azzjgtTJKVKCY1MRt72Mv82O30jhdeWbuTtLD0WTJ2/hoffWBGrPLlg+649PLd4PRDtdfXSQZ/t3A3A65WfRMg9HkyRGDnng3Xb+Olj74ROZ2sjuefT7bsKct/z7pvN1+6clVXayx99m/95clGs8kQZMXixYmM0a6tM02LFNM1tiqTESDauu6prqKkpopqUQ2ytJDw791Sz4TP/0zdnLPqYYTfOZM6HmwLn+9aqzUx/d23t52kL1jLw2qfZUYQb+oLQGGpWlIX8uDBFUsLcPG3v8A5jI5PwXPT3t/jSLc/5xpm9PKFAFlR9Gjjfr985i4tT9sX85unF7NpTw/qtpX1kdK5rWDajhzBpZi8v7PSWKZIS5tE5qwp2b1VlcsVqtu/ak7N72Egke154f31e7lO3oa80lX2xux7xI1XRFHpUYorEyIo3ln3CL6Ys4Kan3iu0KBl5Z/WnnPGnV/liV2lOv+SKOBr/xqLsc7HeUF9Hhb9BKek4UyQ5ZMq8KvpOmMbmz+Nb0CyWBbZtjrO7DZ/lbrE2SEP3wvvr2Pz5LrbtTLgDr3ZZN7rpqfdYtGYri9ZsyYWYhuFKXO/q5zv38NrSjfFkliNMkeSQh95YAcCqGHzlqCoz31tHTbFokoDEYQ3j1evdsn03P3iggh8+VMFvpi/mDzM/4OmFa13jGg2JczTh9TM/Mnsl76wOvgbTWAn6Grh1Oh9+cyXn3Te71vdWMWKKJIfE2eZPmVfFDx+q4B+zC7cu4k7hFNvO6sRU1YpPPq91B14Mu3xLhVimttKcHr60ZH09C65rnljIWXe8Hvk+uaYYumfvrP6Uw2+aydT56ecCJkgfURfTupQpkjwQx1znuq07AFi7ZUf0zGKgKBYpa9+jOlmKQaxC8/S74UZlUX7LZEpVZeFHW/je3+ZyY8R1syferuLg/3mG3Xk4tTBf9SVIk59UFA/NWsHvZyxp0BH98d+L14u0KZIcUsgewzurP+XlD3J3Vn0uNnB53quIel6lQD7d1qcqoa07Ejuts3WbnuSmpxbzxe5qtn6xO1I+YchFfU5VUkGyT8apWLmZ21+srC1PLxZUFc+aXyyKRERGicgSEakUkQku37cQkcec72eLSN+U7652wpeIyGmZ8pQEt4jIByKyWEQuj+MZckkhLFvOuuN1xt0/J+/3zSfJd9NGIYXn5D+8zM7diRFE2H2yi9ZsKZib9WK2OvvLy8t9v7/s0bfzJElmIisSESkD7gBGA4OBb4nI4LRoFwCbVbU/cBtwq5N2MIljd4cAo4A7RaQsQ57fA3oDg1T1YGBS1GfIFXF2cgq9xj5pzipO/+OrtZ/dpkP6TpjGt//6Zuz3zvSyC8Uxx+3Hjt3VOd1zUyhSf5ktzggibO/+jD+9xul/ejVzxEZOsddhP+IYkQwHKlV1uaruItGwj0mLMwZ40LmeAoyQREs0BpikqjtV9UOg0snPL8+LgBtVtQZAVfOz8yqHzKosbtM+gAmPv8t7azP3Gmctq7/DNt8KsFh7mCf89kUGXzcj6/Sqyl9fWc76reHWyPwadbevht/yHOfdOzv4DVKKO9m3KCXPPflbIymhQsmCOBRJT2B1yucqJ8w1jqruAbYAnX3S+uV5IPBNEakQkadFZEAMz1BQvh3gxa21jkl5+4thSsetMUruMSnEvcPnoaH9RG35YnfoheD1GfxeZWLZhm3cMn1x6PWPsGW0/rOdvBaiY5NaBZs4FbLUTNSN6MShSNyas/Sa5BUnbDhAC2CHqpYDfwXudxVKZLyjbCo2bMjdonMQ4mjw3d7NYn1fJ89dnTlSDCR7eSLZl8XDb65k0P88wwUPzOWDdZ8FSnPYr57lp5PCezOOwu7qxANu2xGfko6jXqZOcQYZkdTUKLOWFf8IPG686ufQG2bUeYco1hc6AHEokioSaxZJegHphtC1cUSkKdAe2OST1i/PKuBfzvUTwFA3oVT1HlUtV9Xyrl27hnykeMiN24Vwb/+8lZvYuaeux/165UY+y2ANEk6e2LLKXoYI01n/rKgC4Pn31zPytlcCp5sW0sQ2Ksm6FLa8/apgpvq55YvdGS2HUilzhPObTrv/9Q/59l9DTJ2lEcbBZBgK1YZv3bGH+177ECCUa/x8mEaHIQ5FMhcYICL9RKQ5icXzqWlxpgLjnOtzgBc0UdumAmMdq65+wABgToY8/w2c7FyfCHwQwzOUDGGnts6+6w2Ov/VFANZ/toPv3Duby9OsPV5bupG+E6ax8KPw5oSuI6XQuRQ/FSs28W4Ic8vVm7Zz6PXZr4mkk+0ce5CFb69qdNivnmXoDc8GvlfdiMT7nh9u/Dxwfum8ufwTzrzde3Pjqk+2h7b+ylc/KM53Yt7Kzfz+2SUx5hidyIrEWfO4FJgBLAYmq+oiEblRRM50ot0HdBaRSuAKYIKTdhEwGXgPeAa4RFWrvfJ08poInC0i7wK/AS6M+gxxsLu6hjfTXDlnW3nWfPoFfSdMi821RHJ+Pmme+daq+vk+t3gdQKhzKYpgIFJPieXaFPicu9/gq7e/Fjj+fxas4bM8rRVFJd1AIgzi86nf1dOyzteNqs3+Hm5P+N2LWVt/5WIxPFd18ey7ZmU0Dc43sewjUdXpqjpQVQ9U1VucsOtUdapzvUNVz1XV/qo6XFWXp6S9xUl3kKo+7ZenE/6pqp6hqoeq6jGqOj+OZ4jK72csYew9bzI/hsb/1aWJNZ1/zF4ZOS83tkTY6LV1x25+M31x4KF13K/n9l17+NItz/Hq0g2xKI9cvezFYj3mV/4PzFoBJDoSMxZ9XO+73zwd7Kwbv/ILO1109eML8n5YW96stmKaO5tckZ/1x7DYzvaYSC7UbtwW/YCfQs3XBrnt72cs4S+vLOff7yTOYH/+/fUNrLRueuq9nO2ZWL7hczZ8tpOJT79f+3J6tQWV67fRd8I05q1seDb472csoe+EeHrMu6trGijWuBuoujWScBm/vzaYAcGatPMsCtHjfXTOalZv9nZMmMs2P/2dO/SGGfzggbm+adZ8+gXPLPzYN07c/GLKgrzeLyimSGLC7wXPdoHUrVcbRcfE0bglnSKmumv/6ysNG53ZyzdFPn7VT6EG6bjO/tB7yub2FyuBeNxMDLl+BsPTTiNskrORTji+evtr9RbMX1u6kUv+8VaDHnK2A4GwI69FGdYw/H7zL3JwnK/Xe/vZjj0ZDwc7647X+fHf58UuUyliiiRm6s3bRxxa1PPVEymn+Ai6uP79B+Zy7MQX4rknyu0vLGXL9t21exXSy9atrOOaXpqU4STKXXtq2Ly9/nRh0Hvv2lPDuq07uPOlythGSOkk18YAzrtvNtMWNLQ4y7aurtyUunieOY9s/Wd9sm0n1/57YVZp3Vi3dQevePiiy1QWL76/nkdmr/TcG3TNE+/m7Lf0Ip9+ydxoWtC7lxC79tTw9qrNHHVA59Bps23QPt9VzaX/eIsbzhySVfqgZDdSyZxoU0wHeq3bupPfP/sBH6zbxkUnHQgkFJrbdE8uvBJPePzd0GmCivGzye+4NuyZqNq8nV17ajiga5vQad3Its+zY3fuzFD/9voKBnRvw5hhPVnncyb8hxs/p2PrZqHy/todr7Nmyw7Gn3AAUF8FLsmwn+j7Gaa8HnE56iG1fKs2b6dXx9aBZQ3CDf95j7Kywo0LbEQSkFufeZ9v3vNmRhPZoA3Iqk+854KTle4/89fw1IK1/Pn5pbXN9qsRTkpLbWQ3fb6L3zy9mD0h7dFzuTj522fer9eTS7/X5zv31I1IPHq/97yyjJoabbB4nG+aBCyoGQHn2NP3kRx364uc/IeXsxEtNl5akj71E2/luP3FSn4y6R0q129zrXef7djNzj3VfOX3L3HK/4YrizXOcQxuEtfkcIvG65UbOe7WF3nSWWOMkwde/zD2PINiiiQgycX0TzL0st16dm4vgd+iYnojqdT1mNLt8Fds/DyrtYjrnlzIX15ezotL6ob36bum3ZRN2PNQwkyZ3PnSMv+8qFt7SJ3TF6krn4UfbeWR2Sv50cPzCmprH1ThBi2d1F38cbA97fz6bMxfv/e39J55gDwCyJ9eZ349fbHrcx96w7N87Y5ZAGzcVvdejrn9Nf6W1qied+9szr5rls89M8sVhWT5Lnb81c1fHb8L+EJOf5siCYnXe5BrK0K3HvYXu6s56fcv8fN/hreAdls0v+25+ns7b5i6iL+8vJznUxYdc3nGSTp+StlPQSV7m1Gn1rZs3531TuqgI5L0M+ZTn2tyxWoufmQeKz+p6zxkM0365Ykv8EzaEcQXPFhfCfgttl/8SPQF5WUbtgUa/bqJ4We44OZIdH7VFn71n/qHa71WudHVeq9IrLTjIW199vYXlkbaABoGUyQxE0evwK2NdLN2ScYL6mQv7DuTVDb5tu3fuG0nU+ZVMXdF/Q2SiUY2ObXl3Yv0Cl/pM53oxnn3zfbdSe3G65UJLwHvfxzM7DadVNl/MWUB09/9uJ77/mzYVV3TwP3Gm8vTy9Y7/fR3o08TjvjDy/zm6fcDxU1f58rHaZz58M778ZYdPJYnP3SbPt/F75/9IJwn5wjYYntMuNX1uIbLceUT3gzZ+8a5fLe/fucsVm1q2OinTm2llokEPJDkyn/O5+wjewWSYcfuat7NwmXMU87C+apNmXuCW7YHs7T5fFe1Zx3oO2EaT112HIf0bB9YRjfy0ZDO+XBTVgOAJlI8GzzDkCqzKpx//2yWrk+cHplr3Zj8NXNhMu2GjUgCEqUxj1ppHn4zNzvcixU3JQKJ6Zdk77RGNaeN3/G/fTF0mpnvraM6xErtFZMbehD2eiK/XfxT5lUFvqdXXczXJtggt0mftgw6TRiFRR9lf0JjUt5L//GWp9mvKnyyLR4rRk85Uq7zrXZNkYREBK5/cmFO7cRz9U4H7dXd++pyPvrU369RVN6t2sJLS9aH8i4LHiOSEK9NprNSLnnkLTZ9vosNWZwf8sOHKpjseBMOItPHLodUZTJOEIi00dMv+/tfi2b1k8x74UdbPZ8jyC+V7sKnSRPJWQ9++85EWcbhzfkpHzNupb4Sz5uzyDz1EGxqKwsefCMxQtjyxW7at6pvv576w2XdY47w4z/xdhUnDuwW5mb1Gr2PPv2Cm6ctZsq8KvbvnLB1j/ISuz3J0nWf1TpA/HL/zpx9RC/+77mlmfPSOllrVNOcNsbzwkx7d61vozJ7eTAHh9mWmeeIJOVhb5lW3w9WEMWSqUrNWraR1yuzd96YTlDz6/SOhKo2WPhvIrmb2IpjtK8a9PfO3zghH+tKqdiIJAKV61MXVIP/cOu2Jty5x83qTdv52WPzQ1nZpC8KJy1rPt+1J5Q+C7MgvyHFH9mSj7fx83/O95zOCkL6O5PLKa9v3hPfmfRZ9RdEWPFJ/fWXSXNX1ztzJkNyV95a6W+dFtZaL6hj0CAHhGXrbiZX/t6yJdUP3+7qhEeDXJPucSFXmCIJSLJx+u59c+rCXBqC8Q/P44td/i91rkzydjlKwGsXsFsjkj4SSD5TmHnpXXtq+PkUdxNktzJKzTvMsazpUYvFbUy2uMnvaYnm/PdyhbFjdw3/mlfluWE208+ZaVF23P1zfL9vcD9XP3HaoKe88pPM70ITkYz7t9wYfN0M3l5V3+R33srgRyUEJUg9XLSm/u/y4BsrOerXz8crR0rlqfLZp5YLTJFEQEkceLR91556L+qcNLPVdVt3cvfLy2p/6ExtZyEbyDqHkf6NT+oI5O6Xl/H4W+47de95peEmw9Rsa7Rh4+Itm6bsI6l7cQT46NNgvbtMjvgKTbLD4jW37dcJufKf8/mvPwc/MyWnxLwhc2yWI8H5qz/luffW1X4++643ssonKp/n+WyasGbrUYlFkYjIKBFZIiKVIjLB5fsWIvKY8/1sEemb8t3VTvgSETktRJ5/FpFtccgfhDUuDdXHW3Zwzt1v8N//rO/aeeFHWxJnKzhvycV/n8fEp99n2YZgI5Go62NRRjy1jbOIrxzPB2yQf/+s/wGWYabEvOQREd5bE8xU9/JH347slTjJXS8ti30xM5nd3BWbXcOj5psv3NydC8KKtLoZRK6os/0XPlRR7/ORN82MmGN9gtSBEj6OPRCRF9tFpAy4AziVxHnqc0Vkqqqmbi29ANisqv1FZCxwK/BNERlM4hjdIcB+wHMiMtBJ45mniJQDHaLKHga3xvm3MxIbrBav3cqB3eqc5/1uRsI1R5c2LYDEPgAIN40TNy8tWU/l+sx6N9mu53KpLnUEEqZIAsfNUzHf+sz7nDwojGFDffwaoG/8Jb3nXMTDWIfdGToF23ftYU9anHztvE4lm2my6hrlhw9V8LXDewZOk+f17pJ3kTIcqFTV5aq6C5gEjEmLMwZ40LmeAoyQRGsyBpikqjtV9UOg0snPM09Hcf0O+EUMskdi9SbHRNazwqS7v/DO6zfT6yxxoiw8e/G9v83l5mnup97Vr/C51ySp96tWDXwrNx9khSbdxUlUVOH9j8PvacjHhsJU3GT8hcc6WZIgnoJ/PT3Y7vegxFUq67bu4IX313PZo28HvkdjH4WkEoci6Qmk7vuvcsJc4zjnsW8BOvuk9cvzUmCqqkY3/I4JAd99B0F6Jn9JORzqvizt+cO0/Zk2pqU5qchKHs97p1yHXWxP9bW13jEqEIrvpc3WS/OUt6oY9X8NXaJkOrEw0/PH3Tt2kzGToggyBfTc4nUZ4xSC5PHXhjtx7CNxq6LpNcYrjle4m4JTEdkPOBc4KaNQIuOB8QB9+vTJFD0SIsI7Ac5qr11ILYq+tDtJyZqI5EzKVJ9XYTr06f61vvVXZwFW8j+NEAdu7arXWs+z7/k3sIdnmPffvnMP97yyLJLCzbfPtVoi/LZxVYur/uV9Jo332l1KnJjk8KOQnak4RiRVQO+Uz72ANV5xRKQp0B7Y5JPWK/xwoD9QKSIrgNYiUukmlKreo6rlqlretWvX7J4sIu69e3/yZd2RWuk+2vxFg/DUlyCu40QHXvM0j8xeyZVZeCsGfN/G3dWFeYvmZ+kd2ItcNQaf76qOPG10wC+nxyRN/iiWLltGS80YfvhCdlDjUCRzgQEi0k9EmpNYPJ+aFmcqMM65Pgd4QRMlNxUY61h19QMGAHO88lTVaaraQ1X7qmpfYLuq9o/hGSKRSVEkF5eD1JUh18+ILlAAUkV5JmUXcu25Fzlwk7eruoYb09x7Q3ajiWJpIK7O4vTEqEQ53KyQFMtvFjeKZnT1s/6z3G8+/GKX+9TiG8vi81jgRWRF4qx5XArMABYDk1V1kYjcKCJnOtHuAzo7o4crgAlO2kXAZOA94BngElWt9sozqqy5wmvzXtI6JPntv+ZV8en23Dhuu/rxBb4H96TjpdSSPgdF4KUl+ZkXjqqywqYe9D/PRLpfXBTzFGcuKMTUS5gzaZZtSFg1Pjm/bk9UxYpNgUYL71b5m59nUv5xlE3qzvlU8nFaaCy+tlR1OjA9Ley6lOsdJNY23NLeAtwSJE+XOPEcWB2RTD3qpMnjva99yNL12/iRc050nDw6J9w5B+k7fpOkNm67Qh7DWyjSNzSmGi4UK5/t2M0H6xqaYxeb0UCp8+cXXGe+XUl28lINGx6ZvYo/Pu/vB041uvVeLn/2fKwfmtPGDOzYXc3uGBvU9T7WXZnOg48TL1v6WkuofK1e59+vZcGpqVHKb37O9bu9bZQSiLwViXudDzKVWMg9YsWAuUjJwAm/fZFDb3g2L/cqBvcWyWN7s3WUF5Zd1TWBRz5zVmxyP343ZplyzQOzVrBzj/szF8owKh8Uu5KM5OU6sueB3JXN315fkbO8k5giyYDfCCJJvl02p7NtR3yWXslGPVePVIpmunGya09N7Sl5exvF3mn/+p2zeHFJeF9scz7cxCOz967D59IxRRIDYdrGXPQ80t1ORKLWZLm4W/zU3m0pKaejfu0+pbU3kG0tffxtd4eguWBiFibS598/h+cWR3MGWghXMXFiiiQGwp+FXrzUbUgsqBieFPv0SCYynQ9R7L32xk6h6tept71SkPvGhSkSw50S6uYHPUSpWHh0zirP70pdURp1lM4bFB1TJDGwaE14J3vFTrG+BMkphNSeu9dBXqXI7OXxH7xULOxto6296XFNkeSZbXk+4CYsyTWcIL7DsiHq2suvp7t7MG4sfPTpF5kjlSheG+YaK16HvTVGTJHkmarNX+TkvPa9heTGryDWdIYRlu0ZjskOw90vNzwdtLFiisSoR66H45nOBjeMQlK1ufGOCHOJKRKjHnvbPLZhGNExRWIYhuHBFzFOdTVmTJEYhmF48EwePOc2BkyRGIZhGJEwReLDWx6u1hsztiHOMIywmCLxYVZlaZ5EFwVbbDcMIyyxKBIRGSUiS0SkUkQmuHzfQkQec76fLSJ9U7672glfIiKnZcpTRB5xwheKyP0i0iyOZ3DDGlXDMIzMRFYkIlIG3AGMBgYD3xKRwWnRLgA2O+er3wbc6qQdTOI89iHAKOBOESnLkOcjwCDgUKAVcGHUZ/CiWdO9b8BmutMwjLDE0VIOBypVdbmq7gImAWPS4owBHnSupwAjJHGIxxhgkqruVNUPgUonP888VXW6OgBzgF4xPIMrTYvVBa5hGEYREYci6QmkHhhe5YS5xlHVPcAWoLNP2ox5OlNa3wWeifwERh1rYQn/AAAgAElEQVQ2JDEMIyRxKBK3bnt6c+QVJ2x4KncCr6jqq65CiYwXkQoRqdiwYYNblIwU+uTDQmBWW4ZhhCUORVIF9E753AtY4xVHRJoC7YFNPml98xSR64GuwBVeQqnqPaparqrlXbt2DflIzn2ySmUYhlE85GOGPg5FMhcYICL9RKQ5icXzqWlxpgLjnOtzgBecNY6pwFjHqqsfMIDEuodnniJyIXAa8C1VrYlBfk/2wgGJYRiNjHzMrDSNmoGq7hGRS4EZQBlwv6ouEpEbgQpVnQrcBzwsIpUkRiJjnbSLRGQy8B6wB7hEVasB3PJ0bnk3sBJ4wymgx1X1xqjP4cbeqEd2V9vUlmE0JvIxIomsSCBhSQVMTwu7LuV6B3CuR9pbgFuC5OmExyJzEPbGNRLDMBoXTfLQju19GyVCYHrEMIxSJx/tmCkSH0yPGIZR6tTkdCU5gSkSHzq0bl5oEQzDMIoeUyQ+HD+gS6FFMAzDiIRNbRUYscktwzBKHFMkhmEYRtFjisQPG5AYhlHimPmvYRiGEYlTDu6e83uYIvHB9pEYhlHqjDu2b87vYYrEMAzDiIQpEsMwDCMSpkh8sJktwzCMzJgiMQzDaNTk3qO3KRLDMAwjEqZIfDA38oZhGJmJRZGIyCgRWSIilSIyweX7FiLymPP9bBHpm/Ld1U74EhE5LVOezqmJs0VkqZOneVY0DMMoIJEViYiUAXcAo4HBwLdEZHBatAuAzaraH7gNuNVJO5jEaYlDgFHAnSJSliHPW4HbVHUAsNnJOyfYeMQwDCMzcYxIhgOVqrpcVXcBk4AxaXHGAA8611OAEZKYNxoDTFLVnar6IVDp5Oeap5PmZCcPnDzPiuEZDMMwGiWah9Oz41AkPYHVKZ+rnDDXOKq6B9gCdPZJ6xXeGfjUycPrXoZhGIZDPs5VikORuM0ApetArzhxhTcUSmS8iFSISMWGDRvcomTE1toNwyhlrjh1IP27tcn5feJQJFVA75TPvYA1XnFEpCnQHtjkk9YrfCPQwcnD614AqOo9qlququVdu3bN4rEMwzBKmyP6dMzLfeJQJHOBAY41VXMSi+dT0+JMBcY51+cAL6iqOuFjHauufsAAYI5Xnk6aF508cPJ8MoZncMUOtjIMo5TJ16xK08xR/FHVPSJyKTADKAPuV9VFInIjUKGqU4H7gIdFpJLESGSsk3aRiEwG3gP2AJeoajWAW57OLa8CJonIzcDbTt6GYRhGGvnqCkdWJACqOh2YnhZ2Xcr1DuBcj7S3ALcEydMJX07CqsswDMMoAmxnuw+22G4YhpEZUySGYRhGJEyRGIZhNFbyNKtiisQwDKOR0nmfFnm5jykSwzAaJece2avQIhSUw/t04KAebfNyL1MkPthiu2GULleMHFhoEQrK8L6d8nYvUySGYTRK9m3fqtAiFJSubfMzrQWmSHyxne2GYRiZMUViGIZRgvTrsk+hRajFFInhy5+/dXihRTAMw6FdyzpnJC/+/CTfuPk8KtwUiQ+22G4Y8XLlqXv3AnhUfnfuYYHiHdqzPWcN2y/H0tQRi68twzAMI/f07tg6Y5zRh/TgrvOOzIM0ddiIxDCMvGGj/Ow5/dAeDN6vXcZ4LZrmv1k3ReJDpjp/wXH98iKH0XjYp3lZTvP/wZeLu07mc96+sbF/52CL64UoY1MkEfjl6QcXWgSjxLh8xIDAcX984oGh82/bMrvZ6lvPPjSrdAATRg/y/f77X+6bdd5GHUHVQz6O1k3HFIkP1nsy4qZ3p8xz3EmCTGOk061ddpvQDuiafeOT6S25/qtDss7bqCNoc3RRFh2QqERSJCLSSURmishS57/rAcEiMs6Js1RExqWEHyki74pIpYj8SZyW2ytfEfmOiCxw/maJSDATBsMADuvVvtAiMPqQHoHjJk6WDsdx/buETpPkqcuOyyqdn5RTfnxMvc/WN8s9TZqU3tTWBOB5VR0APO98roeIdAKuB44icbLh9SkK5y5gPImz2gcAozLk+yFwoqoOBW4C7okof0Fp36pZzu8xJo8mgGEpi1Dhrz0j/LRimyynfeIkzCj3qH6dw+cfwRvDIT3jV7TlAf09/e83SrtP+LtzhvKlvq796NgoZk8bURXJGOBB5/pB4CyXOKcBM1V1k6puBmYCo0RkX6Cdqr6hia7XQynpXfNV1VlOHgBvAjl175npZyven7WO847ev9AieBKl/C48/gA67dM8NlmKkVbNwi/MF6LHH2bg5NYYDurRlq8fUdqees8t780/f3xs7efUPRxRRomlQlRF0l1V1wI4/7u5xOkJrE75XOWE9XSu08OD5nsB8HQk6QtMPl76L+XRA2hYmuyl8xxXjfJfnE6ivpNG7mRbpFnMotWlDSHn3vKTp5ZIIaaa8k1GRSIiz4nIQpe/MQHv4VaK6hOeOUORr5BQJFf5xBkvIhUiUrFhw4ZAgjbMI6tkjYpcNvZNy6LlXao/z3eO7sMpB7v1jTIzcnB33++z/b2yWY/JxCv//ZUGYaXwm325f/gpxXRyUJxF3R5lVCSqeoqqHuLy9ySwzpmiwvm/3iWLKqB3yudewBonvJdLOH75ishQ4F5gjKp+4iP3PaparqrlXbt2zfSYgTioe34OiSkWBvVoy8gh/g1XFM47en++d2zfrKZwoGGv4/2bRrnGKzbatWzGveO+lDFeamN0wsCuLL4x8/OFaWyOOaCuwYzS7nk1mn06N7RQc5MvF41uFL5R3jtzpAzE8UgDCmDGmy1Rp7amAkkrrHHAky5xZgAjRaSjs8g+EpjhTFl9JiJHO9Za56ekd81XRPoAjwPfVdUPIsoemmymGkqZ//mvwTQry52FeMtmZdxw5hB+eMIBAAzsHu3FaZlBIeVzsfJ7x/aNNb+mTYRWzct8a2CfTq3rjUgy9axTTYWLqTGvuPYUDt43vOlzMRHHCK9H+5b1PgepvXd954jI982GqK3EROBUEVkKnOp8RkTKReReAFXdRMLCaq7zd6MTBnARidFFJbCMujUP13yB64DOwJ0i8o6IVESU35d0C5v0uhF1qJmvZu3bR/XJ053Ckf78p2aYtolKoToCvToGO2DpopPq2/+Hlfa5K04MVae+c1RDQ4ybxgzhDwEdA2ZDkKm3Lm1aBH6Ojq1zb/mYDYXSy6NCmJfHSSRFoqqfqOoIVR3g/N/khFeo6oUp8e5X1f7O399SwiucabIDVfVSx3rLL98LVbWjqg5z/sqjyG/A8QOKx6IkbCfOrbGZfvnxscgCCd9GcXDmYcFMsIPsZPcro+ZNm3iaFx/Rp0O9zysmnsHwfnWGGEkl+91j+lIe0ow1tff966/575BPVV5tWjStd+9sKFqDjZRHyqeEhdpEbTvbQ1BTTOP/mHj4gqMyxvlujkyI0+t82NJ1i++3Gzzs1FZcI6SgjV36viL36RH/Ukq9VWryjGWbGjfkD5Eav2eG0VfLZnVNzr8uOtYzXtD2sFj1SDZeCTLi8bAHdW/Lt4b3LuiG28Lv0CohGpsamTT+aNdwkfqNwzVnHMzxA7ow/uF5sd4/vWFvHnA95sISc5Z5SM9Eo3LGofu6fn/ukb04/5i+DcLD1Ldk3mHb1UN6tmPhR1vr3cvtvscP6MKrSzcGztdrpOvWY3ZTXMEVRHFqklMO7s7vZiyJtGCeXlZuT/rejafRomlZpM29cWAjkghEHUYW2pfX0Qe4L8am96BbNitj5JAenHd0vGst2T5+cod62OSZFuNzxahD9mXZr0/n9m+7nzZ5SM/2HOrSm6w3onA+uDW6KyaewR3OImvyt0v3Muw1yujRLrGgm6khunec9yxyth2s5O/vlj7o6PHYA6Ob6qZz8qD6ptlH9etEH8dH2iMXZh7Bp7Ovs2j+zzR3MXHQunnTgisRMEUSjpiHJP1THOUNLQI/UJm48tSD+GYMppFJ3PxOff2Ini4x3Un+HAd23Yfbvpl5gdjPw23rHLt3L2sioTsOLZqFfz1Tb3FLhvUKgFvPHsrPThnIUf38N662aOpePs/89PjQU2GXndyfow/o5KsqghbVb88ZGu7mAWjbslkDi7eubRMWbs0DnvWRKv/1Xx3CH8cOC705OBd7e3KFKZIQxPmzPnzBcP56fnHaCkja/yQd92nOrTG+uMmXM5VubVu6xPRn8o+O4WuH121Jmn/dSJbc3HDPRec23p5xD9mvoSJ36xXHPSoD90bzqcuOo13LhhZJmepgUlkp0K9L3fkVXuk6t2nBT04ZUE/JhWnABvVoR3XIBu/KkQcxabx77/yrjmFCUJWbq1Fm6iNFnTho1byMMcO8O0grJp4RKr9rzziYUUMKY53lhSmSEITtIfiZJh4/oCvtU74v/OA0Xnq0a8nvIiidX4w6yPO79AY+/Vdp37qZZw86Cv+66BhuPiv7czu8cPvt0x0oXvyV/kDmOhjHbGnYDlNNTTxdrGW/Pp0/jR2W+ODzICcMjGeDsR8awfigXj4e4UHOjWmwRuJ8vPD4A7j7u/k9SjcTpkhCELY+jRwcoteQVmnS57ijmOlmalu6tKnv/DCq65Ik5xzZy7dhc3tBc73X445vH8HTPzk+q5MKj9w/Xr9l3wmxv6d8/4RJbiYnnMk1kgZlG6I1DNtwhh2RNLxfIn3q9J9fDfz28PimV71Irbci7vIs/NVpABzWu4PLt/7M/NmJGeOkdxoas/ffvYq4pizdTpRLXS87YWBXHs5iUc+LdLEX/eo0FjkvAcCrvzi59vrnIwcyLIsXww0RCTQEd3s9wrw0YV6vM4buy8H7tmvQIJeK14IRB/ubJCfLIv15cvl0qSOS5N6Q5CK+H36L7XGtGf77ki/z2lUNfX6FQRBXGdu0aMqcX45g0g/drR/9SN+1XuqYIglBXI2N38azM4buy0M/GM4RfepvCvvqYfu5rilkwz4tmrJPi7qhdauU3vmlJw+IpedTMg1zioVO9yxPF0ySy535QRfqPUckOaQ6RZEcuX9H/jh2GL8aE+RURO9nuvaMwTFIBsN6d6BXx+CnUoalW7uW9d6fXJKN8UW+KF7JGgFxNaaVt4zmG+W9mfyj+M0HUwljMZUN4084gJvPOiTygV7pL1RcbeaJaXPvubbOrpU7xht5ZdU0lIlouBJN3wk/ZlhPWjevvwaQsFry2DHvcrug1lF7A93bteCyk/vH7r8tTuzXCsGRfaKfgPZ/3xzmGu63+7mps1HPa0QShyXRsl+fXutjKVejiW5tW3De0fv7t5sBbp2rF+rGMYfwaBbTFG6EMfXMhb5KFuMfncXr1BFoxrQhf/5Rh7hvtExlzLCe9Q5+gjrl5iXb0ltGhxMkC5oFWA/MRs/H+Zu2bFbGlSMPKtg+qCCYIgnIqCE9mDD64FALpG4vZEavpj4vsVflvPmsQ/lHxDWVbPY5ZEuQhspPlPQXKpPUfTu39nRVnypKy2ZlHBNhg1vqc50xNHPjmo/ppw6tw58ima9Zsf07t2bC6EH8xcMCydvzdHz1NPk+pvtVS61/HYrUMWQxYS5SApI0txvWuwOPzF5VG96/Wxsq128LnE+u2uo4X/5Y1kjcLLIyCJmrBuyFK0/KUc7pZPcEcdaJZF5xZDmwexs+WBe8bodFRAI5qmyYLkYZnP/pdTP181nDevKXV5bXxv/HhUfldN2lVrZidSTmgo1IQnLOkb2Y+bMTWHDDSABaxDSXKw0uXOK4fJfc2evXSIetjrma2ko6vQz7fhxzQGd+PnJg6PsN6tGW6/5rME2aiOdxp24NyVcDeusNQr69VzQva8IlXzmQxy9OTCN5NZR+9O/ahu9/uS9/+W7xbJgdMyy+3yQsI1MsDxU4tn8X10O74iZp/lsK6sQUSUhEhAHd29buOr77vCO57OT+wdNniuCrEBqm9jvN7XDHdXhor7pOgjA9ommXH5c53wyfvcK+eth+XHrygMCyJHnmpyfwgxw4eHzwB8MZ7xzGlUr6OtfjFx/La1ed3CBeHQ2fNojZrB8iwn+fNoghLjv1g9KkiXD9V4fU2xnvxnH96/Y2PXHxsYHc1GRLr46tOCWD6XOctAmxppQtPdq1pNM+3lOPSbc9B3Qt/pMSIykSEekkIjNFZKnz33U1WkTGOXGWisi4lPAjReRdEakUkT85JyVmzFdEviQi1SJyThT546B3p9ZcObLhLmyvXd1ebXPc5yp89bD9eOLiL2eVNtm8hREpSMMVpleceuucjPB99jB0dl7ufZo35fUJDRXBiQO78svTD24Qnr6R84g+HdmvQ+ZDrVI7CDN+doLrPd0YMagbE78e/077ILx/0yge+H7dccGH9+lYz01N3KR6KvCqDsd4OCH14m/f+1Jt5Uqvm/26JpTo5SE6iWGZNeFkKq45xfP7Hu1a8tAPhtcaTBQzUUckE4DnVXUA8LzzuR4i0gm4HjgKGA5cn6IY7gLGAwOcv6SDJM98RaQMuJXEEb5Fy7nlvcONBGobNu9U6Q3qq7+o22jlmy7lumUQW/QcLVaky1isQ/arRg3i5rMOYcTB3egZQBEkKWsS7nVyU6ztWzXLeM+kmfbRB3Rm7HB/4w9xqVdxmHm3bFZWa02YDxIjwPoFll5OndoEMyzYv3Nrnvnp8XxlULfaOni6h3FEq+bhRiZhOj1+U65JThjYlbYuPteKjajjtzHASc71g8BLwFVpcU4DZiZPORSRmcAoEXkJaKeqbzjhDwFnkThu1y/fy4B/AXXdoZIivuazd6e6edogvf0j+nTgD9/I3LtJNjqZRkmtmpXxxe5qjzxcwtIXNJ3/mV6+zh7D/we/P5y/v7nSd3ogE263btW8LKMrEjfC7dVIkSFksg6tmgdOlxztpJb9/waoA8VGqqWeiFBx7SkNLPFuPHMI0xaszZjXy/9d1wFLlmG6UvrRCQeycuP2BsdUZyryF688iaUhjG8aC1G7FN1VdS2A87+bS5yewOqUz1VOWE/nOj3cM18R6Ql8Dbg7otwFw6u3mdyklwt/OslDjyaePTTjvDekrpG4f9+hdTNGDenBnGtG1Au//duHNzhq1S0P320kLhrRa8f4ob3ac+s5Qwtu3ZI0Xw27IJwP899cFE0+nCZmokubFg32n/h5d85M/R+j0z7Nufu7R4bePNu3yz5ZeziI6lmhkGQckYjIc4Cbw6RrAt7DrSqrT7gf/wdcparVmRoPERlPYtqMPn3id/2dLenuFNq0aMq2nXs4dXB3Zr63LtTUVlCO7d8llKvqTD/CO9eNdA3/r6H7sX7rDn75RF1Y+1aZRwvDnc17R/XrxEtL1gPpTvNyryiitOnHDwhXvlEZd+z+zFz8caCz4Pd3rItOG9KDWcs+CX2vv55f3sAA4KEfDA+dTxxEUbzd27Vg3dadDcKzsWoLQ1CP4U9ddhz7tm/J9l3VTJ2/ht/NWFLwDlIYMioSVfVcDRKRdSKyr6quFZF9gfUu0aqom6YC6EViqqrKuU4NX+Nce+VbDkxyCrgLcLqI7FHVf7vIfQ9wD0B5eXnW1eT5K09k1Sfbs0obpA79+5JjmbFoHfuEnIttcK8G987+zUimjcN0tVvKbnwvd+PH9u/Col+dxj4tmtYqEjcuO7k/T7z9UXShckiXNs0bnLAXN/t33qeeo00/enVszaJfnUbr5mVcP3VR6Hvl0n9YtmRTLadffjxH3vxc5HtH1Te/PXuoqwv55LEBncn9IWu5IOoayVRgHDDR+f+kS5wZwK9TFthHAler6iYR+UxEjgZmA+cDf/bLV1VrbTlF5AHgKTclEicHdm3DgTk0v+vfrS39u7Vl1rLEedipG50O79OBj7fsqP3sN+0V52lqde199pokKU6qVH4SBnHhceXIg1wt5KKQtLSK8vKm/i4V154aOF1y9JnrfmcY9yjFzA1nDqFV8zKO8zlS4aDubVmy7rMG4dGmveLjG1/KvQv8QhC1hk0EJovIBcAq4FwAESkHfqyqFzoK4yZgrpPmxuTCO3AR8ADQisQi+9N++TZmjj2wC/eeX15v/jndfDffI92g96t3VopPmiD7APJ9uuhhvTswtFcH3/04uaaEZjAKSu9Orbn920f4xnnikmMZfF1wg87UEyUDxQ+cc/aU0Am7tURSJKr6CTDCJbwCuDDl8/3A/R7xDgmab1qc74WXOL+E3SF+SoRphDjrXq01VYC47980ytdaKfnNT0YMYPB+GfyM1UuXv9b1kq9E2yuQrSIoxQaj2En3OpzKNacfzFML1tQLy/Uayd6C7WwvIVLbq2vPaLghLi5qXTMEaCGj7Cdwm467+Cv9GX1ID76Z41Pwium0uWKSpTHzwxMO4MlL63tgKMbRYB/HrL9/t+Lf0Z6kcUyeFilnHrYfj78V3+JwsmFvIolzm3NNtHfMz/rMO+dO+zTnrvNyfx51qRy8FSfTLj8uslFHYyXoGmM+as0pg7vz+MXHcnhMJ5XmA6tVOeSkg7rRrEzYXR1v9XPNLcZbZNpH4keQ3nWchgGlTL5LIYr/rcZK7YbNAsuRTvoJqcWOKZKYKd+/I6s315kLx9lm+s3nxnlGd62X3izGJMn1kmI/k7qoppOKSJTGwKE929c6LM1IyLK3n8odUyQxM+WiYzNHyhK/EUKcDWOUEUnHfZrzx7HDOPZAbxPNUtpoBTD/upFIDlYTS21g9sxPj2f+6k8LLUZG/nNZZk/USa4ePYhfTFnA0F7xjtZKrIpHxhRJCTJqSENHA8cP6MJ3j96f/Tu35uZpiyPl/6MTD+Ank96p58srDGOG+TsFbBLQ5PLFn5/Ehs8a7kbON+0znJAXtdEolTZnUI92DOoR3PIuDq4ePch1X0hcHN6nIzOvODH2fPfvvA8nHdSVn54S/hydUsQUSQkhIsz+5QjXoz+bljXhprMOYer8NS4pwzFmWM+MyiAK//jhUTz+1keezhiT9OuyTyDfYKXK3rjgH5YfZXGCYi5IemgIem56s7ImPPD9wriSKQRm/ptjHvj+cEYf4uaqLDu6t2tZ72yGdIq1d5vaZA7s3pYJoweV3BRXrrByKH5uPWcovz1naK0rE6M+NiLJMccN6MJxA7rQd8K0QotSEIq9jYxjnaKoFu73Ql79xVdoEeScnQi0a9msoN4Pih1TJHni8YuPteamiCgKBWczW7GQ7VqeER+mSGLiuStOoE0L70XZUrMLj4titUwqJrmKQacZRhRMkcRE/25tCy1CfYqooYTG3Vhm7WsrXjEMo2DYYnsjoyimbFwotkazmMqpmGQxjGywEYmRU6yR9OaKUweydssXRXl41N6K1dfsMEVi7JUkDyvr2zn6PpVs257enVozafwxke9vxMOcX46geVObpMkGUyTGXsnZR/Skf7c2DCshD6tGbunWrrj9wxUzkdSviHQSkZkistT572qaJCLjnDhLRWRcSviRIvKuiFSKyJ/E2Znll6+InCQi74jIIhF5OYr8Ru4pJuuoVETElIhhxETUcdwE4HlVHQA873yuh4h0Aq4HjgKGA9enKIa7gPHAAOdvlF++ItIBuBM4U1WHsBccwZst5n4jf9jOdGNvJ6oiGQM86Fw/CJzlEuc0YKaqblLVzcBMYJSI7Au0U9U3NHFAxUMp6b3y/TbwuKquAlDV9RHlN3KMtbHR+PGJB9LC5u2NIidqDe2uqmsBnP/dXOL0BFanfK5ywno61+nhfvkOBDqKyEsiMk9Ezo8ov2EUNRNGD2LJzaMLLYZh+JJxsV1EngPcvA5eE/Aebn1S9Qn3oylwJDACaAW8ISJvquoHDW4qMp7EtBl9+vQJKGrpY36f8o+VuLG3k1GRqOopXt+JyDoR2VdV1zpTVW5TTVXASSmfewEvOeG90sKTPtC98q0CNqrq58DnIvIKcBjQQJGo6j3APQDl5eW2YGAYhpEjok5tTQWSVljjgCdd4swARopIR2eRfSQww5my+kxEjnastc5PSe+V75PA8SLSVERak1jAj3aKUyMjedLb1w/vlSFmfjjUcbt9qLnfNoxGS9R9JBOBySJyAbAKx4pKRMqBH6vqhaq6SURuAuY6aW5U1U3O9UXAAySmqZ52/jzzVdXFIvIMsACoAe5V1YURn6FR0btTa1ZMPKPQYtQy4uDuvD7hZHp2aFVoUXKGGRQYezuixWroHyPl5eVaUVFRaDGMRkbyjJkPf3O6mQAbjRIRmaeq5ZnimV2hYRiGEQlTJIYRERuNGHs7pkgMI0taNSsrtAiGURSY00bDyJKpl36Zl5ZsKLQYhlFwTJEYRpYM6N6WAd2L7GRMwygANrVlGIZhRMIUiWEYhhEJUySGYRhGJEyRGIZhGJEwRWIYhmFEwhSJYRiGEQlTJIZhGEYkTJEYhmEYkdgrvP+KyAZgZZbJuwAbYxQnV5SCnCZjfJSCnCZjPBRSxv1VtWumSHuFIomCiFQEcaNcaEpBTpMxPkpBTpMxHkpBRpvaMgzDMCJhisQwDMOIhCmSzNxTaAECUgpymozxUQpymozxUPQy2hqJYRiGEQkbkRiGYRiRMEXig4iMEpElIlIpIhMKLMsKEXlXRN4RkQonrJOIzBSRpc7/jk64iMifHLkXiMgROZTrfhFZLyILU8JCyyUi45z4S0VkXB5kvEFEPnLK8x0ROT3lu6sdGZeIyGkp4TmrDyLSW0ReFJHFIrJIRH7ihBdNWfrIWDRlKSItRWSOiMx3ZPyVE95PRGY7ZfKYiDR3wls4nyud7/tmkj3Hcj4gIh+mlOUwJ7wg705gVNX+XP6AMmAZcADQHJgPDC6gPCuALmlhvwUmONcTgFud69OBpwEBjgZm51CuE4AjgIXZygV0ApY7/zs61x1zLOMNwM9d4g52fusWQD+nDpTluj4A+wJHONdtgQ8cWYqmLH1kLJqydMqjjXPdDJjtlM9kYKwTfjdwkXN9MXC3cz0WeMxP9hh/by85HwDOcYlfkHcn6J+NSLwZDlSq6nJV3QVMAsYUWKZ0xrqUcD4AAANKSURBVAAPOtcPAmelhD+kCd4EOojIvrkQQFVfATZFlOs0YKaqblLVzcBMYFSOZfRiDDBJVXeq6odAJYm6kNP6oKprVfUt5/ozYDHQkyIqSx8Zvch7WTrlsc352Mz5U+BkYIoTnl6OyfKdAowQEfGRPRZ85PSiIO9OUEyReNMTWJ3yuQr/lybXKPCsiMwTkfFOWHdVXQuJlxzo5oQXWvawchVK3kudaYL7k1NGxSCjM71yOIlealGWZZqMUERlKSJlIvIOsJ5Ew7oM+FRV97jcr1YW5/stQOdcy+gmp6omy/IWpyxvE5EW6XKmyVPodx0wReKHuIQV0sTty6p6BDAauERETvCJW2yyJ/GSqxDy3gUcCAwD1gJ/cMILKqOItAH+BfxUVbf6RfWQJ+dyushYVGWpqtWqOgzoRWIUcbDP/QpWjulyisghwNXAIOBLJKarriq0nEEwReJNFdA75XMvYE2BZEFV1zj/1wNPkHhB1iWnrJz/653ohZY9rFx5l1dV1zkvcg3wV+qmLQomo4g0I9FAP6KqjzvBRVWWbjIWY1k6cn0KvERiTaGDiDR1uV+tLM737UlMg+atTqbIOcqZPlRV3Qn8jSIpy0yYIvFmLjDAsfZoTmIhbmohBBGRfUSkbfIaGAksdORJWmmMA550rqcC5zuWHkcDW5LTI3kirFwzgJEi0tGZFhnphOWMtDWjr5Eoz6SMYx1rnn7AAGAOOa4Pzrz8fcBiVf3flK+Kpiy9ZCymshSRriLSwbluBZxCYi3nReAcJ1p6OSbL9xzgBU2sYnvJHgsecr6f0mkQEus4qWVZFO+OK/le3S+lPxKWEh+QmGO9poByHEDCgmQ+sCgpC4m53OeBpc7/Tk64AHc4cr8LlOdQtkdJTGfsJtE7uiAbuYAfkFjQrAS+nwcZH3ZkWEDiJd03Jf41joxLgNH5qA/AcSSmJBYA7zh/pxdTWfrIWDRlCQwF3nZkWQhcl/IOzXHK5J9ACye8pfO50vn+gEyy51jOF5yyXAj8nTrLroK8O0H/bGe7YRiGEQmb2jIMwzAiYYrEMAzDiIQpEsMwDCMSpkgMwzCMSJgiMQzDMCJhisQwDMOIhCkSwzAMIxKmSAzDMIxI/D+ETPuw7EAEDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#len(cnt)\n",
    "qmid,qsiz=40,15\n",
    "nor1,nor2=1/cur1.sum(),1/cur2.sum()\n",
    "fac=100 #10 ms\n",
    "cfunc=np.correlate(cur1[(qmid-qsiz)*fac:(qmid+qsiz)*fac]*nor1,cur2*nor2,\"valid\")\n",
    "plt.plot(cfunc)\n",
    "cmax=np.argmax(cfunc)\n",
    "#pl.xlim(cmax-500,cmax+500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in square\n",
      "  \n",
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:7: RuntimeWarning: overflow encountered in exp\n",
      "  import sys\n",
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in multiply\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[450.88056261203815,\n",
       " -11.388929219115294,\n",
       " 1.8921621674756457,\n",
       " 34.7070448591319,\n",
       " 7.65488254502368e-05,\n",
       " 2.317882755296653e-05]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#fitting the maxima for sub-bin precission\n",
    "def fit_corr(cfunc,csiz=200,doplot=True):\n",
    "    cmax=np.argmax(cfunc)\n",
    "    p0=[cmax,np.log(cfunc.max()),300,250,0]\n",
    "    from scipy import optimize as op\n",
    "    x=np.r_[cmax-csiz:cmax+csiz]\n",
    "    fun1=lambda p:np.exp(-(p[0]-x)/p[2]+p[1])*(x<p[0])+(x>=p[0])*np.exp(-(x-p[0])/p[3]+p[1])+p[4]\n",
    "    bst=op.fmin(lambda p:((cfunc[cmax-csiz:cmax+csiz]-fun1(p))**2).sum(),p0,disp=0)\n",
    "    #pl.xlim(cmax-500,cmax+500)\n",
    "    if doplot:\n",
    "        plt.plot(x,cfunc[cmax-csiz:cmax+csiz],x,fun1(p0),x,fun1(bst))\n",
    "    #pl.plot(x,fun1(p0))\n",
    "    chi2=((cfunc[cmax-csiz:cmax+csiz]-fun1(bst))**2).sum()\n",
    "    return list(bst)+[chi2]\n",
    "\n",
    "fit_corr(cfunc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:8: RuntimeWarning: overflow encountered in square\n",
      "  \n",
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:7: RuntimeWarning: overflow encountered in exp\n",
      "  import sys\n",
      "/Users/toast/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in multiply\n",
      "  import sys\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (298,) (400,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-47e0a69ae132>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mbin2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcur2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlcurve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtevs2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msel22\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfac\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfac\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mnor1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnor2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mcur1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mcur2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfit_corr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrelate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcur1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqmid\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mqsiz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfac\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqmid\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mqsiz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfac\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnor1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcur2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnor2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"valid\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdoplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-48-99fd85f1ee40>\u001b[0m in \u001b[0;36mfit_corr\u001b[0;34m(cfunc, csiz, doplot)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mfun1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m>=\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mbst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfunc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfun1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdisp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;31m#pl.xlim(cmax-500,cmax+500)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdoplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfmin\u001b[0;34m(func, x0, args, xtol, ftol, maxiter, maxfun, full_output, disp, retall, callback, initial_simplex)\u001b[0m\n\u001b[1;32m    407\u001b[0m             'initial_simplex': initial_simplex}\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_minimize_neldermead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0mretlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fun'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nit'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'nfev'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36m_minimize_neldermead\u001b[0;34m(func, x0, args, callback, maxiter, maxfev, disp, return_all, initial_simplex, xatol, fatol, adaptive, **unknown_options)\u001b[0m\n\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m         \u001b[0mfsim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msim\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m     \u001b[0mind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfsim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda2/envs/py36/lib/python3.6/site-packages/scipy/optimize/optimize.py\u001b[0m in \u001b[0;36mfunction_wrapper\u001b[0;34m(*wrapper_args)\u001b[0m\n\u001b[1;32m    291\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mwrapper_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m         \u001b[0mncalls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper_args\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mncalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-48-99fd85f1ee40>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mfun1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m>=\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mbst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfunc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mcmax\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mcsiz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mfun1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdisp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;31m#pl.xlim(cmax-500,cmax+500)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdoplot\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (298,) (400,) "
     ]
    }
   ],
   "source": [
    "fac=100\n",
    "# testing small time shifts in both curves = change of binning\n",
    "res=[]\n",
    "for i in range(10):\n",
    "    dt=i*0.1/fac\n",
    "    bin1,cur1=lcurve(tevs[sel2]+dt,fac,-2*fac)\n",
    "    bin2,cur2=lcurve(tevs2[sel22]+dt,fac,-2*fac)\n",
    "    nor1,nor2=1/cur1.sum(),1/cur2.sum()\n",
    "    res.append(fit_corr(np.correlate(cur1[(qmid-qsiz)*fac:(qmid+qsiz)*fac]*nor1,cur2*nor2,\"valid\"),doplot=False))\n",
    "res=np.array(res)\n",
    "res[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f88d28add60>]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dt,dt2=0,2.\n",
    "bin1,cur1=lcurve(tevs[sel2]+dt,fac,-2*fac)\n",
    "bin2,cur2=lcurve(tevs2[sel22]+dt+dt2,fac,-2*fac)\n",
    "nor1,nor2=1/cur1.sum(),1/cur2.sum()\n",
    "pl.plot(bin1[(qmid-qsiz)*fac:(qmid+qsiz)*fac],cur1[(qmid-qsiz)*fac:(qmid+qsiz)*fac]*nor1)\n",
    "pl.plot(bin2[(qmid-qsiz)*fac:(qmid+qsiz)*fac],cur2[(qmid-qsiz)*fac:(qmid+qsiz)*fac]*nor2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2499.76181528, 2500.18839719, 2500.02423928, 2499.44702508,\n",
       "       2499.45858562, 2499.67846459, 2500.11168383, 2499.93440118,\n",
       "       2499.55382381, 2499.76181528])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shifting 2nd curve wrt 1st one\n",
    "fac=100\n",
    "gres=[]\n",
    "lcshift=np.r_[0:200./fac:10j]\n",
    "for dt2 in lcshift:    \n",
    "    res=[]\n",
    "    for i in range(10):\n",
    "        dt=i*0.1/fac\n",
    "        bin1,cur1=lcurve(tevs[sel2]+dt,fac,-2*fac)\n",
    "        bin2,cur2=lcurve(tevs2[sel22]+dt+dt2,fac,-2*fac)\n",
    "        nor1,nor2=1/cur1.sum(),1/cur2.sum()\n",
    "        res.append(fit_corr(np.correlate(cur2[(qmid-qsiz)*fac:(qmid+qsiz)*fac]*nor2,cur1*nor1,\"valid\"),doplot=False))\n",
    "    gres.append(res)\n",
    "gres=np.array(gres)\n",
    "gres[:,:,0].mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "pl.errorbar(lcshift,gres[:,:,0].mean(1),gres[:,:,0].std(1)/np.sqrt(10),fmt='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.00545489, 0.85076897, 0.28109647, 0.69282169, 0.87527583,\n",
       "        0.87665498, 0.92077662, 1.01057198, 0.68109019, 0.7133978 ]),\n",
       " array([0.59381367, 0.90006216, 0.89952512, 0.60114414, 0.87905743,\n",
       "        0.02751533, 0.90702352, 0.86531695, 0.72736948, 0.59381367]))"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gres[:,:,0].std(0),gres[:,:,0].std(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### something (completely) different"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/quicklook/glg_lc_chan12_bn200224416.pdf', 'https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/quicklook/glg_lc_chan34_bn200224416.pdf', 'https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/quicklook/glg_lc_chan567_bn200224416.pdf', 'https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/quicklook/glg_lc_chantot_bn200224416.pdf', 'https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/quicklook/glg_lc_tot_bn200224416.pdf']\n"
     ]
    }
   ],
   "source": [
    "# directory listing on the server\n",
    "if False:\n",
    "    import ftplib\n",
    "    ftp = ftplib.FTP(\"heasarc.gsfc.nasa.gov\")\n",
    "    ftp.login(\"anonymous\", \"ftplib-example-1\")\n",
    "\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def get_url_paths(url, ext='', params={}):\n",
    "    response = requests.get(url, params=params)\n",
    "    if response.ok:\n",
    "        response_text = response.text\n",
    "    else:\n",
    "        return response.raise_for_status()\n",
    "    soup = BeautifulSoup(response_text, 'html.parser')\n",
    "    parent = [url + node.get('href') for node in soup.find_all('a') if node.get('href').endswith(ext)]\n",
    "    return parent\n",
    "\n",
    "url = 'https://heasarc.gsfc.nasa.gov/FTP/fermi/data/gbm/bursts/2020/bn200224416/quicklook/'\n",
    "ext = 'pdf'\n",
    "result = get_url_paths(url, ext)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tipovat GRB200412381, GRB200125864, GRB200313071 - nejjasnejsi"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}