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1 | <?xml version="1.0" encoding="utf-8"?> | |||
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2 | <notebook> | |||
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3 | <name>rmt</name> | |||
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4 | <nbformat>2</nbformat> | |||
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5 | <worksheets> | |||
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6 | <worksheet> | |||
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7 | <cells> | |||
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8 | <markdowncell> | |||
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9 | <source># Eigenvalue distribution of Gaussian orthogonal random matrices</source> | |||
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10 | </markdowncell> | |||
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11 | <markdowncell> | |||
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12 | <source>The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices | |||
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13 | from the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest | |||
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14 | neighbor eigenvalue distribution $\rho(s)$.</source> | |||
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15 | </markdowncell> | |||
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16 | <codecell> | |||
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17 | <input>from rmtkernel import ensemble_diffs, normalize_diffs, GOE | |||
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18 | import numpy as np | |||
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19 | from IPython.parallel import Client</input> | |||
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20 | <language>python</language> | |||
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21 | <prompt_number>1</prompt_number> | |||
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22 | <collapsed>0</collapsed> | |||
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23 | <outputs /> | |||
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24 | </codecell> | |||
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25 | <markdowncell> | |||
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26 | <source>## Wigner's nearest neighbor eigenvalue distribution</source> | |||
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27 | </markdowncell> | |||
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28 | <markdowncell> | |||
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29 | <source>The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution | |||
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30 | for the GOE: | |||
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31 | ||||
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32 | $$\rho(s) = \frac{\pi s}{2} \exp(-\pi s^2/4)$$</source> | |||
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33 | </markdowncell> | |||
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34 | <codecell> | |||
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35 | <input>def wigner_dist(s): | |||
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36 | """Returns (s, rho(s)) for the Wigner GOE distribution.""" | |||
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37 | return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.)</input> | |||
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38 | <language>python</language> | |||
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39 | <prompt_number>2</prompt_number> | |||
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40 | <collapsed>1</collapsed> | |||
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41 | <outputs /> | |||
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42 | </codecell> | |||
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43 | <codecell> | |||
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44 | <input>def generate_wigner_data(): | |||
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45 | s = np.linspace(0.0,4.0,400) | |||
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46 | rhos = wigner_dist(s) | |||
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47 | return s, rhos</input> | |||
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48 | <language>python</language> | |||
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49 | <prompt_number>3</prompt_number> | |||
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50 | <collapsed>1</collapsed> | |||
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51 | <outputs /> | |||
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52 | </codecell> | |||
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53 | <codecell> | |||
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54 | <input>s, rhos = generate_wigner_data()</input> | |||
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55 | <language>python</language> | |||
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56 | <prompt_number>4</prompt_number> | |||
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57 | <collapsed>0</collapsed> | |||
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58 | <outputs /> | |||
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59 | </codecell> | |||
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60 | <codecell> | |||
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61 | <input>plot(s, rhos) | |||
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62 | xlabel('Normalized level spacing s') | |||
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63 | ylabel('Probability $\rho(s)$')</input> | |||
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64 | <language>python</language> | |||
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65 | <prompt_number>17</prompt_number> | |||
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66 | <collapsed>0</collapsed> | |||
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67 | <outputs> | |||
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68 | <output> | |||
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69 | <output_type>pyout</output_type> | |||
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70 | <text>&lt;matplotlib.text.Text at 0x3828790&gt;</text> | |||
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71 | <prompt_number>17</prompt_number> | |||
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72 | </output> | |||
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73 | <output> | |||
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74 | <output_type>display_data</output_type> | |||
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360 | UQpVRUpDUW5qenpUZkxuT2U1NTU1RHI5ZlRyVnMzRGg4K1RIWjJObDI2ZEFIZ3M4OCtZOFNJRVF3 | |||
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361 | YU5JaUFnQUErK09BRDA5OWRzMllOClBYdjJKRFEwbElrVEp6SisvUGh5T1gvOTlWZjY5ZXRIY0hB | |||
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362 | d1hidDI1ZUxGaTFYZU8rRkVhckowcmhEWHV1R0dHNVR6NTg4clhsNWUKU2w1ZW5qSnIxaXdsTmpa | |||
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364 | d0lFRApsYUtpSXVXenp6NVRicmpoQmlVbEpVVXBLaXBTL1B6OFRFdkg1K1RrS0lxaUtFVkZSVXBZ | |||
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365 | V0poeThlSkZSVkVVeFdBd0tOdTJiVk1VClJWRzh2THlVczJmUG12SVpqVWJsemp2dlZCSVRFMDNI | |||
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366 | NXVibUtvcWlLQys4OElMeTVaZGZLb3FpS0lHQmdVcDZlcnB5OGVKRkpUSXkKc3NMbG9kZXZYMi9h | |||
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367 | WitEVFR6OVY1c3lab3lpS292ejU1NTlLYUdpb29paUs4djc3N3l2VHBrMVRGRVZSVHB3NG9keCsr | |||
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368 | KzNYUGI3MApPVXViTm0yYTh2SEhINXQraDRLQ0F1WHc0Y09LVHFkVHZ2bm1HNld3c0ZDNS8vNzds | |||
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369 | UlVyVnBTNU4xZXVYRkVHRHg2czdOMjcxM1N2CjQrUGpUZmN0UHo5Zk9YejRzR2twK1U4Ly9WUnAz | |||
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370 | YnExY3VMRUNlWDgrZk5LdTNidGxNdVhMeXRHbzFIeDh2SlNEaDgrckp3OWUxYnAKMnJXck1uNzgr | |||
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371 | SEk1UjQwYXBheGJ0MDVSRkhVWjhDdFhycFE3UmpndmVjSVFWdGUwYVZNZWUreXhNdjg2QmZqdmYv | |||
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372 | L0xtREZqQUJnNwpkaXlyVjY4RzFDYWtCeDU0Z0taTm01cU9mZUNCQjZoZnZ6NDllL2FrZWZQbTlP | |||
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373 | M2JsL3IxNjZQWDYwMHJBU2NuSnhNVkZZVmVyK2ZRCm9VUDg5Tk5QVldhTGlZbWhYNzkrUkVWRnNX | |||
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374 | M2JObmJ1M0luQllFQ3YxNU9ZbUVocWFpcC8vUEVIaXFJUUdocEtreVpOR0Q1OGVKWEwKVlgvOTlk | |||
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375 | Y3NYTGdRdlY1UFpHUWtwMDZkNHZEaHd6ejAwRU9zV0xFQ2dPWExsNXY2Q2lvNi90Q2hRNVdlUHlR | |||
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376 | a2hMaTRPTjU1NXgxeQpjbkpvMkxBaG9DNUxNM1RvVUJvMGFNRElrU05aczJZTkFGdTNibVhZc0dF | |||
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377 | RUJnYVNtWm5KMnJWcnVYejVNdXZYcitmSko1OEUxT2JEClJvMGFsYnRXZUhnNGJkdTJwV25UcHZq | |||
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378 | Nys1T1ptVWxhV2hwZHVuVEJ5OHVMRmkxYWNPKzk5MVo0VDNyMjdNbVVLVk9ZTjI4ZVY2NWMKb1c3 | |||
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379 | ZHVsWCtOeEhPdytHWEJoSE9hZUxFaVhUdDJwWFJvMGVYK1hsbEg3eHQyN1l0ODdwa2ZhNzY5ZXZU | |||
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380 | dkhsejA4L3IxNi9QNWN1WAp1WERoQWxPbVRHSGp4bzNjY3NzdERCMDZsSFBuemwwMzAyZWZmY2F4 | |||
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381 | WThlWVAzOCtvQzVUSFJBUXdQcjE2OHNjZC96NGNmTit5VktLCmk0dUpqNCtuVDU4KzVkNXIyYkls | |||
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382 | TzNic1lQbnk1YWFsR1NvN3ZtUzVqbXRGUlVYUnJWczNGaTllVEs5ZXZmanFxNi9LM0pjU0pmMDMK | |||
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383 | NDhlUFo4V0tGUVFFQkRCcDBpVE9uVHVIVHFlcmNJWFNhMTE3dndzTEM2bFhyMTZadnFIS3poRWRI | |||
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384 | YzJBQVFOTVM1R2twNmR6MDAwMwpYZmQ2d25uSUU0YXdpUnR2dkpHSEhucUlmLy83MzZZUG1rR0RC | |||
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385 | dkg1NTU5VFhGek1KNTk4d3IzMzNsdXRjeXVLUW01dUxtNXViclJwCjA0WjkrL2J4NDQ4L1h2ZnZi | |||
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386 | TnUyamRtelovUEZGMStZZmhZU0VzS3BVNmRNVHl5WExsMWkvLzc5dEd2WGpycDE2NUtSa2NHbFM1 | |||
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387 | ZFkKdm54NWxaa2VmdmhoRWhJU3VIRGhBa0NaSmErSER4L09PKys4dy9uejV3a0lDS2p5K0lvY1Bu | |||
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|
388 | ell0SFpSdjM3OVRQMGllWGw1ckZ5NQprcUtpSXBZdFcwWmtaQ1NGaFlWY3VIQUJMeTh2L3ZqakQx | |||
|
|
389 | YXRXZ1dvL1V0MzNYVVhDeGN1UkZFVWlvcUtLQ2dvcVBKMzArbDA5T2pSCmd4MDdkcENkblUxT1Rn | |||
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|
390 | NkppWWtWRG1nNGVQQWczdDdldlBycXEvajYranJFWGlYQ2VxUmdDS3NxL1NIeTNIUFBjZWJNR2RQ | |||
|
|
391 | cnlaTW4KYy9Ub1VmUjZQYWRPbmVMWlo1K3Q4TzlkKzdxaTk5cTNiOCt3WWNNSUNBamdtV2VlcVhR | |||
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|
392 | b2FzbS9xdVBqNHpsMzdoeDMzWFVYZXIyZQpwNTU2Q29BdnZ2aUNCUXNXRUJnWXlCMTMzTUh2di84 | |||
|
|
393 | T3dFY2ZmY1JMTDcxRTc5NjlDUW9LcXZERFVhZlRtWDcrd0FNUEVCb2FTa1JFCkJBRUJBVXliTnMx | |||
|
|
394 | MDNBTVBQTUN5WmN0NDZLR0h5dnlzb3VOTG43TzA1Y3VYRXhBUVFFaElDUG41K2FaeitmcjY4dDEz | |||
|
|
395 | M3hFY0hFeEEKUUFCUlVWRTBiTmlRS1ZPbUVCb2F5dkRod3hrMGFKRHBQRys4OFFZSERod2dLQ2lJ | |||
|
|
396 | WHIxNm1RWWVsRnl6c3V2WHJWdVhlZlBtTVh6NApjQ0lqSStuU3BRc2RPM1lzZDl5Y09YUG8wcVVM | |||
|
|
397 | b2FHaCtQcjZjc2NkZDFUNDMwVTRKMWw4VUFnbmxaMmR6ZURCZzlteFk0ZGRybmZwCjBpV2FOR2xD | |||
|
|
398 | WGw0ZTk5eHpEeDkvL0RHMzMzNjdYYTR0SElQMFlRamh4T3k1NDF4c2JDenIxcTNEemMyTi8vdS8v | |||
|
|
399 | NU5pVVF2SkU0WVEKUWdpelNCK0dFRUlJczBqQkVFSUlZUllwR0VJSUljd2lCVU1JSVlSWnBHQUlJ | |||
|
|
400 | WVF3aXhRTUlZUVFadmwvQ0hyZjBuUWF1Ym9BQUFBQQpTVVZPUks1Q1lJST0K | |||
|
|
401 | </png> | |||
|
|
402 | </output> | |||
|
|
403 | </outputs> | |||
|
|
404 | </codecell> | |||
|
|
405 | <markdowncell> | |||
|
|
406 | <source>## Serial calculation of nearest neighbor eigenvalue distribution</source> | |||
|
|
407 | </markdowncell> | |||
|
|
408 | <markdowncell> | |||
|
|
409 | <source>In this section we numerically construct and diagonalize a large number of GOE random matrices | |||
|
|
410 | and compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core.</source> | |||
|
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411 | </markdowncell> | |||
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412 | <codecell> | |||
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413 | <input>def serial_diffs(num, N): | |||
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414 | """Compute the nearest neighbor distribution for num NxX matrices.""" | |||
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415 | diffs = ensemble_diffs(num, N) | |||
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416 | normalized_diffs = normalize_diffs(diffs) | |||
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417 | return normalized_diffs</input> | |||
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418 | <language>python</language> | |||
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419 | <prompt_number>6</prompt_number> | |||
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420 | <collapsed>1</collapsed> | |||
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421 | <outputs /> | |||
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422 | </codecell> | |||
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423 | <codecell> | |||
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|
424 | <input>serial_nmats = 1000 | |||
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425 | serial_matsize = 50</input> | |||
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426 | <language>python</language> | |||
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427 | <prompt_number>7</prompt_number> | |||
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|
428 | <collapsed>1</collapsed> | |||
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|
429 | <outputs /> | |||
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|
430 | </codecell> | |||
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|
431 | <codecell> | |||
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|
432 | <input>%timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize)</input> | |||
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433 | <language>python</language> | |||
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|
434 | <prompt_number>8</prompt_number> | |||
|
|
435 | <collapsed>0</collapsed> | |||
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|
436 | <outputs> | |||
|
|
437 | <output> | |||
|
|
438 | <output_type>stream</output_type> | |||
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|
439 | <text>1 loops, best of 1: 1.19 s per loop</text> | |||
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440 | </output> | |||
|
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441 | </outputs> | |||
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|
442 | </codecell> | |||
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443 | <codecell> | |||
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|
444 | <input>serial_diffs = serial_diffs(serial_nmats, serial_matsize)</input> | |||
|
|
445 | <language>python</language> | |||
|
|
446 | <prompt_number>9</prompt_number> | |||
|
|
447 | <collapsed>0</collapsed> | |||
|
|
448 | <outputs /> | |||
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|
449 | </codecell> | |||
|
|
450 | <markdowncell> | |||
|
|
451 | <source>The numerical computation agrees with the predictions of Wigner, but it would be nice to get more | |||
|
|
452 | statistics. For that we will do a parallel computation.</source> | |||
|
|
453 | </markdowncell> | |||
|
|
454 | <codecell> | |||
|
|
455 | <input>hist_data = hist(serial_diffs, bins=30, normed=True) | |||
|
|
456 | plot(s, rhos) | |||
|
|
457 | xlabel('Normalized level spacing s') | |||
|
|
458 | ylabel('Probability $P(s)$')</input> | |||
|
|
459 | <language>python</language> | |||
|
|
460 | <prompt_number>10</prompt_number> | |||
|
|
461 | <collapsed>0</collapsed> | |||
|
|
462 | <outputs> | |||
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|
463 | <output> | |||
|
|
464 | <output_type>pyout</output_type> | |||
|
|
465 | <text>&lt;matplotlib.text.Text at 0x3475bd0&gt;</text> | |||
|
|
466 | <prompt_number>10</prompt_number> | |||
|
|
467 | </output> | |||
|
|
468 | <output> | |||
|
|
469 | <output_type>display_data</output_type> | |||
|
|
470 | <png>aVZCT1J3MEtHZ29BQUFBTlNVaEVVZ0FBQVl3QUFBRU1DQVlBQUFEWGlZR1NBQUFBQkhOQ1NWUUlD | |||
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677 | Znl5Uk9mTU9UcklSeThlbERyT0VKVUt1dThEcU4xSkZ5VThRdWhqU2ZhUDRGU2lzQjFnV3g4WnFP | |||
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680 | Tk9oQTFPWXFmei83TXVCL0drWldicFhVa0ljck5xczZTMHVsMDBIazEKT0lmRGQ2VWRCckNrTTNr | |||
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681 | czc0d2h5OWxPNVdVcDYwL0kzdDZCdE15Yk1BeW9DM3dEWlB4M2VmMzZqVWhOVFM1ak8wSlVIamxM | |||
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682 | cXBBTQplQXN6azVaMkUzSVViTXlEeTYvQzgyMmg4UjhVbnJtblB4VmNDRE5uaFFWRHhpK0VtVkky | |||
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684 | NEdldEF3bGhHT3ZhdzNEbTd1RW9uY1pCUlBWUkE1MU9kOTlIaWE0K0NpR0gKUWVrZ0NMbklUMWdF | |||
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|
685 | NnlvWXJaSHhDMkZpdVJTZlRjREEyUVd5NjhHUG44SXZNSGp0WU43Zjl6NzVLci9xSXd0UlR0WlhN | |||
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|
686 | R1Q4UWxpYQo0eEE3SlpZdHA3Y3c0S3NCWEVtOW9uVWlJVXBrTlFYamF0cFZxQTBrZE5BNmloQkdh | |||
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|
687 | OTJ3TmJ2SDc4YXZ0UitlSDNueTcvMy9KaWN2ClIrdFlRaFJoTlFVajhtSWtYS0xnVEJRaExGQU5t | |||
|
|
688 | eHE4NmZjbSt5ZnZKK3g4R0o0ZmViTHIvQzZ0WXdtaFp6WGZycEVYSStHaTFpbUUKcUxoSEdqL0N6 | |||
|
|
689 | Ly96TXd2NkxtRHlqNU1adVhFa2wxTXVheDFMQ09zcEdCRVhJeUJPNnhSQ1ZBNmRUc2RUcms5eDRz | |||
|
|
690 | VVR1RG01NFJYaQp4WUk5QzhqSXlTajd6VUpVRWFzb0dBbnBDY1NueHNNTnJaTUlVYm5xMk5WaHZ2 | |||
|
|
691 | OThZcWZFRW5zMWxvZVhQc3pDL1F0SnowN1hPcHFvCmhxeWlZT3k1dEllZUxYdUNuSkVvckpSTEl4 | |||
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|
692 | ZStIL1U5UC8zUFQwUmRpY0pscVFzTDlpd2dOYXZFVzBvS1VTV3NvbURJL2J1RlpTdjcKNGo5N2V3 | |||
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|
693 | Y0F2SnA1c2ZHWmpld2V2NXVUaVNkeFdlTEMzUEM1SkdmSTVJV2k2bGxGd1lpOEdDbDNOUk1Xck95 | |||
|
|
694 | TC8vNDZRYUc3a3p0ZgpEZnVLcU9lanVKSjZoWWVYUHN5a3paUFlkMm1mM0hkRFZCbUxuOTQ4K1U0 | |||
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|
695 | eXJSYTNJbWwyRXJWcTFNS2NwcjIybk8yWVV4YjV6S1cxCnVkK2Y2dlhiMS9uUHIvL2gweU9mWXFP | |||
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|
696 | ellaTFhKTVo1anFOcHZhWmxyRmRVUjlWMmV2TjlsL2ZoKzZBdk5XMXJhaDFGQ00wMHE5ZU0KMlQx | |||
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|
697 | bmMrckZVNndldXBvVGlTZG92N3c5dzlZUEkvUjBLTG41dVZwSEZGYkE0dmN3Z25jRVkxL1RuamY5 | |||
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|
698 | M3J3NzBadjUvSXZRY3JaagpUbG5rTTVmV3h0Zy8xYlNzTk5ZZlg4K25SejdsVE5JWkF0b0dFTmd1 | |||
|
|
699 | a0lGdEIrSlEyOEdvZFFuclV0NDlESXN2R0Q2cmZYaS8zL3Y0Ck9mdEp3YkNLTFBLWlMydFRrVC9W | |||
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|
700 | K05SNGZqcnpFMXZQYkNVOExweE9UVHNSMkM2UXdIYUJlRFR4S0gxV1hXR1ZxbTNCcVB0T1hSSm4K | |||
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|
701 | Si9KQWpRZWtZRmhGRnZuTUpiT2pZSEM4ZEliZTZqVXpONVB3dUhDMm50bksxdE5ieVZONURHdzdr | |||
|
|
702 | TzRQZGNmM1FWL2FPN2JIUm1meApSNnZGZlZUYmd0SDdzOTVFVG96VVB6ZWZQM0JMMm80NVpaSFBY | |||
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|
703 | SkUyeHY0NUs2VTRsWGlLSGVkMkVIMGxtcGdyTVNUZVNlVFJGby9pCjg2QVBQZy82NFB1Z0w4M3JO | |||
|
|
704 | emRxdmNLOGxiZGdXUHdkOS95Yy9iU09JSVRGMHVsMHVEbTU0ZWJrcG44dDhVNGlzVmRpaWI0U3pj | |||
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|
705 | ZUgKUHViNUg1K250bDF0ZkI3MHdjM1JqYllPYldubjBJNTJqZHZoVk1kSkRtZFZJNXJzWVVSR1Jo | |||
|
|
706 | SVVGRVJ1Ymk0elpzeGcrdlRweGRxOAo5dHByckYrL25rYU5HckYyN1ZwY1hWMkx0ZEhwZE93NHU0 | |||
|
|
707 | UCtEL2ZYUHpmUGZ4R0dBLzRtMkU1RjIreW01SnltekdJdGV4amgvTGN2CnpYY1BJenc4SEg5Ly8v | |||
|
|
708 | dTJVVXB4L3VaNVlxL0c4a2ZpSDV5OWVaWXpTV2M0bTN5V25Qd2MyanEwL1c4UmNXaW5mKzVVMTZu | |||
|
|
709 | U0RtMFoKa3RNY1dFcE9pOXJEbURsekppRWhJYlJ1M1pxQWdBREdqQm1EbzZPamZubE1UQXg3OXV6 | |||
|
|
710 | aDRNR0RiTisrbmVEZ1lFSkRRMHRjVjQrVwpQVXdWdXdMQ0tmMkwySnlFWXhrNUxVRTRsdENYaG56 | |||
|
|
711 | QjZYUTZIblo0bUljZEhpNjI3R2JHVGM0a0Z4U1BNMGxuQ0xzUXhxcURxemgzCjh4eTNNbS9SdUha | |||
|
|
712 | am10UnRRcE82VFdoYXIybkJ6M1h1K2Jsd1dkMm0xTGFyWGFHYzVzQlNjcGFYeVF0R1Nrb0tBSDM2 | |||
|
|
713 | RkZ5WlBXREEKQUtLam93a01ETlMzaVk2T1pzU0lFVGc0T0RCbXpCamVlT09OVXRkWHQyYmRxZzBz | |||
|
|
714 | aE1Xb1ljRGhJVHVnNkkyWjVzK2ZmOS9saHF6agpyK3JYYjhTZG0zZEl2SlBJbitsLzZoODMwbS93 | |||
|
|
715 | Wi9xZm5FaytvLy81ei9RL3VYSDdCanFkam5vMTYxSFhyaTUxYTliVi8xeXZaajB1Cm5MekFsUzFY | |||
|
|
716 | OU04TC8zLzJyRGxrcHQ2QjdMdVJjaW1ZVSs2ZVI5M2E5aHo3N1ZkcTJOUW85V0dyczVWRGF3WXdl | |||
|
|
717 | Y0dJalkwdGNuakoKM2QyZHFLaW9JZ1VqSmlhRzU1NTdUdi9jeWNtSmMrZk84ZkREeGYrRkk0UW9W | |||
|
|
718 | RGpGeVAzODlkRFd2THVQMHBZYnNvN2kwdEowMk5uYQowYngrYzRNR3pKVlNaT1Jta0o2ZFRucE9P | |||
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|
719 | cmV6YjVPZWZmZi9jOUw1ejU3LzROUENSNzhzTlR1VnE3ZXZrdG5rRGp3NEVtcmVMbmpZClpvTk5i | |||
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|
720 | cEZIdXMwcEh2dnlNWEx5Y3NqTnp5M3hrYWZ5c05YWkZpa2lkcloycFJhWXdrTnRPZ3FLakU2blE0 | |||
|
|
721 | ZU9hNGV1c2VYakxmcGwKaFVYb3IrMEtmOWF5WFhtWTVhQzNVcXJZOGJYU1BtVHgxdzNwak1wb1kr | |||
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|
722 | dzY1aHZRcGpLMlU1RTI4eWs5cHltem1QSXpWMldXK1FhMApxWXp0VkxUTlgzL25sYk9keXY0WCs2 | |||
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|
723 | WlZtMHBac3FITTk4WVpjTE9jdkx2L3l5TEx1R0IvY1QzMGVvWGViODVNWGpDNmR1M0tQLzd4CkQv | |||
|
|
724 | M3o0OGVQTTNEZ3dDSnRmSDE5T1hIaUJBRUJBUUFrSkNUZzR1SlNiRjBXZkVhd0VFSllISk5mbmRP | |||
|
|
725 | Z1FRT2c0RXlwdUxnNGR1N2MKaWErdmI1RTJ2cjYrYk5xMGlhU2tKTmF0VzRlYm0xdEpxeEpDQ0dG | |||
|
|
726 | Q21oeVNXcng0TVVGQlFlVGs1REJqeGd3Y0hSMEpDUWtCSUNnbwpDQjhmSDNyMTZzV2pqejZLZzRN | |||
|
|
727 | RGE5YXMwU0ttRUVLSWV5a3pGeEVSb1Z4ZFhWWGJ0bTNWMHFWTFMyd3paODRjMWFaTkc5VzVjMmQx | |||
|
|
728 | Cjh1UkpFeWNzVUZiTzNidDNLM3Q3ZStYbDVhVzh2THpVMjIrL2JmS01FeWRPVkUyYU5GRWVIaDZs | |||
|
|
729 | dGpHSHZpd3Jwem4wcFZKS1hicDAKU2ZuNyt5dDNkM2ZsNStlbjFxNWRXMkk3cmZ2VWtKeGE5MmxH | |||
|
|
730 | Um9ieThmRlJucDZleXRmWFYzMzQ0WWNsdHRPNkx3M0pxWFZmM2lzMwpOMWQ1ZVhtcElVT0dsTGpj | |||
|
|
731 | MlA0MCs0TGg1ZVdsSWlJaVZGeGNuR3JmdnIxS1NFZ29zanc2T2xyMTdObFRKU1VscVhYcjFxbkF3 | |||
|
|
732 | RUN6CnpMbDc5MjQxZE9oUVRiSVZpb3lNVkljUEh5NzFpOWhjK3JLc25PYlFsMG9wZGUzYU5YWGt5 | |||
|
|
733 | QkdsbEZJSkNRbXFUWnMyS2pVMXRVZ2IKYytoVFEzS2FRNSttcDZjcnBaVEt6TXhVSFRwMFVHZk9u | |||
|
|
734 | Q215M0J6NlVxbXljNXBEWHhaYXVIQ2hHanQyYklsNXl0T2ZaajNEMkwzWApiTFJ1M1ZwL3pjYTkv | |||
|
|
735 | bnJOeHNtVEo4MHlKMmcvU04rN2QyOGFOV3BVNm5KejZFc29PeWRvMzVjQXpabzF3OHZMQ3dCSFIw | |||
|
|
736 | YzZkT2pBCndZTUhpN1F4aHo0MUpDZG8zNmQxNnRRQjRQYnQyK1RtNWxLclZxMGl5ODJoTDZIc25L | |||
|
|
737 | QjlYd0xFeDhmejAwOC84Znp6ejVlWXB6ejkKYWRZRm83UnJOdTRWRXhPRHU3dTcvbm5oTlJ1bVpF | |||
|
|
738 | aE9uVTdIL3YzNzhmTHk0dVdYWHpaNVJrT1lRMThhd2h6Nzh1elpzeHcvZmh3ZgpINThpcjV0Ym41 | |||
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|
739 | YVcweHo2TkQ4L0gwOVBUNW8yYmNxMGFkTm8yYkpsa2VYbTBwZGw1VFNIdmdTWU5Xc1dIM3p3QVRZ | |||
|
|
740 | MkpYL05sNmMvCnpicGdHRUlaY2MyR2xqcDM3c3pseTVlSmpZM0YzZDJkbVRObmFoMnBHT25MOGts | |||
|
|
741 | TFMyUFVxRkVzV3JTSXVuV0x6anhnVG4xNnY1em0KMEtjMk5qYjgrdXV2bkQxN2xwVXJWM0xreUpF | |||
|
|
742 | aXk4MmxMOHZLYVE1OUdSb2FTcE1tVGZEMjlpNTFiNmM4L1duV0JhTnIxNjZjT25WSwovL3o0OGVO | |||
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|
743 | MDY5YXRTSnZDYXpZS2xYYk5SbFV5SkdmOSt2V3BVNmNPZG5aMlRKNDhtZGpZV0xLeUtuYUJVR1V6 | |||
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|
744 | aDc0MGhEbjFaVTVPCkRzT0hEK2U1NTU3anlTZWZMTGJjWFBxMHJKem0xS2ZPenM0TUhqeTQyR0Zk | |||
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|
745 | YytuTFFxWGxOSWUrM0w5L1B6LysrQ050MnJSaHpKZ3gKL1BMTEw0d2JONjVJbS9MMHAxa1hERXU1 | |||
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|
746 | WnNPUW5EZHUzTkJYOHkxYnR0Q3BVNmNTajMxcXlSejYwaERtMHBkS0tTWlBub3lIaHdjdgp2ZlJT | |||
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|
747 | aVczTW9VOE55YWwxbnlZbUpuTHIxaTBBa3BLUzJMRmpSN0hDWmc1OWFVaE9yZnNTWU1HQ0JWeStm | |||
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|
748 | SmtMRnk3d3pUZmY4UGpqCmovT2YvL3luU0p2eTlLZFpUZzF5TDB1NVpxT3NuTjkrK3kyclZxMmlS | |||
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|
749 | bzBhZE9yVWlZVUxGNW84NDVneFk0aUlpQ0F4TVpHV0xWc3kKZi81OGNuSnk5Qm5OcFMvTHlta09m | |||
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|
750 | UW13Yjk4KzFxeFpRNmRPbmZEMjlnWUsvbEF2WGJxa3oyb09mV3BJVHEzNzlOcTFhNHdmUDU2OAp2 | |||
|
|
751 | RHlhTld0R2NIQXd6WnMzTjd1L2RVTnlhdDJYSlNrODFGVFIvclRvTys0SklZUXdIYk0rSkNXRUVN | |||
|
|
752 | SjhTTUVRUWdoaEVDa1lRZ2doCkRDSUZRd2doaEVHa1lJaEtaV05qUTNCd3NQNzV2Ly85NzcvY0Fy | |||
|
|
753 | VHErZnY3Yy9qd1lRQUNBd05KVFUydDBQckN3OE1aT25Tb3dhOVgKeGJhcTB0V3JWM25tbVdkTXVr | |||
|
|
754 | MWhtYVJnaUVwVnMyWk52di8rZTVLU2tnRGpyOFROeTh1cmNJWjd0N2wxNjFiczdlMHJ2RTVyMXFK | |||
|
|
755 | RgpDelp1M0toMURHRUJwR0NJU21Wblo4Y0xMN3pBb2tXTGlpMjdldlVxTTJmT3hOUFRrMW16Wm5I | |||
|
|
756 | anhnMEFKa3lZd01zdnY0eXZyeSt2CnZ2b3FFeWRPNUpWWFhzSEh4NGYyN2R0ejVNZ1JYbmpoQlRw | |||
|
|
757 | MDZNQzhlZlAwNi92NzMvOU8xNjVkNmRHakI2dFhyeTR4ajdPek0wbEoKU1h6MDBVZDRlM3ZqN2Ux | |||
|
|
758 | Tm16WnRlUHp4eDRHQ2VjREdqUnVIcjY4dmMrYk0wVitSR3hzYlM5KytmZkgyOW1iNzl1MWxmdTZN | |||
|
|
759 | akF3KwovUEJEL1B6OENBd01KRHc4SElEdTNic1h1WnEyY084bk16T3p4UGFsdVh6NU1vTUdEY0xM | |||
|
|
760 | eXd0UFQwL09uVHRIWEZ3Yzd1N3VUSjQ4CkdUYzNOK2JQbjYvUC8vYmJiK1BqNDBQWHJsMVpzR0JC | |||
|
|
761 | a2ZXODhzb3JlSHQ3MDZWTEZ5NWN1RUJjWEJ3ZE8zWUU0SXN2dm1EMDZORU0KSGp3WUR3OFBsaTVk | |||
|
|
762 | cW4vdnRtM2I2TjY5T3o0K1ByejAwa3RNbno2OVdNNmpSNC9TdDI5ZnZMeTg2Tnk1TTdkdjN5Nno3 | |||
|
|
763 | NFFGcWNqVQp1VUw4VmIxNjlWUnFhcXB5ZG5aV0tTa3A2dC8vL3JlYU4yK2VVa3FwV2JObXFmZmZm | |||
|
|
764 | MThwcGRTQ0JRdlU3Tm16bFZKS2pSOC9Ydm41CitlbW4zSjR3WVlJYU5HaVF5c3JLVWw5ODhZV3FW | |||
|
|
765 | NitlQ2c4UFYxbFpXY3JOelUwL2RYeHljckpTU3Ftc3JDemw2K3VyYnQrK3JaUlMKeXQvZlh4MDZk | |||
|
|
766 | RWdwcFpTenM3TktTa3JTNTh2SnlWRzllL2RXb2FHaCtyYTNidDFTU2lrMWUvWnM5YzAzM3lpbGxP | |||
|
|
767 | clVxWk9Lam81Vwp0Mi9mVmdNSERpeHhldWpkdTNmcjd6UHcrZWVmcXlWTGxpaWxsTHArL2JyeThm | |||
|
|
768 | RlJTaW0xYU5FaU5YZnVYS1dVVWxldlhsWHQyN2UvCmIvdDcxM212dVhQbnFrOCsrVVQvR1RJeU10 | |||
|
|
769 | U0ZDeGVVVHFkVDMzMzNuY3JNekZSUFAvMjArdmJiYjR2MFRXNXVyaG82ZEtnNmRlcVUKdnE5WHJG | |||
|
|
770 | aWg3N2M3ZCs2b0N4Y3U2S2VTLy96enoxV1RKazNVMWF0WFZXcHFxbnJvb1lkVWRuYTJ5c25KVWM3 | |||
|
|
771 | T3p1ckNoUXNxS1NsSgpkZTdjV1UyZlByMVl6dkhqeDZ1d3NEQ2xWTUUwNExtNXVjWGFDTXNsZXhp | |||
|
|
772 | aTB0V3ZYNTl4NDhZVitkY3B3TTgvLzh5a1NaTUFtRHg1Ck1sdTJiQUVLRGlHTkdER0MrdlhyNjl1 | |||
|
|
773 | T0dER0NtalZyMHIxN2R4bzJiSWlmbng4MWE5YkUyOXRiUHhQd3pwMDdDUXdNeE52Ym0vUG4Kei9Q | |||
|
|
774 | TEw3K1VtVzNHakJuMDdkdVh3TUJBRGgwNnhMRmp4L0QzOThmYjI1dlEwRkFpSXlPNWN1VUtTaWw4 | |||
|
|
775 | Zkh5b1c3Y3VvMGFOS25PNgo2azJiTnJGNjlXcTh2YjBaT0hBZ04yN2M0TUtGQzR3Y09aSnZ2LzBX | |||
|
|
776 | Z0EwYk51akhDa3BxZi83OCtWTFgzN1ZyVnhZdlhzeDc3NzFICmNuSXlEenp3QUZBd0xjMndZY09v | |||
|
|
777 | VmFzV1k4YU1ZZHUyYlFBY1BIaVE0Y09IMDZsVEp3NGZQc3lPSFR2SXpzNW05KzdkVEpreUJTZzQK | |||
|
|
778 | ZkZpN2R1MWkyeG93WUFETm16ZW5mdjM2dUx1N2MvandZYUtpb3VqWXNTUE96czQ0T0Rqd3hCTlBs | |||
|
|
779 | TmduM2J0M1o4NmNPU3hmdnB6YwozRnhzYlczTC9KMEl5MkgyVTRNSXkvVFNTeS9SdVhObkprNmNX | |||
|
|
780 | T1QxMHI1NG16ZHZYdVI1NGZ4Y05XdldwR0hEaHZyWGE5YXNTWFoyCk5tbHBhY3laTTRjOWUvYnc0 | |||
|
|
781 | SU1QTW16WU1HN2V2SG5mVEY5ODhRV1hMMTltNWNxVlFNRTAxUjRlSHV6ZXZidEl1L2o0ZU1NKzVE | |||
|
|
782 | M3kKOC9OWnNXSUZmZnIwS2Jhc2NlUEcvUDc3NzJ6WXNFRS9OVU5wN1F1bjYvaXJ3TUJBdW5UcHdw | |||
|
|
783 | bzFhK2pac3ljYk4yNHMwaStGQ3NkdgpwaytmenJmZmZvdUhod2V6WnMzaTVzMmI2SFM2RW1jby9h | |||
|
|
784 | dS85bmRtWmlZMWF0UW9NalpVMmpxQ2dvTG8zNysvZmlxUzZPaG9talp0CmV0L3RDY3NoZXhpaVNq | |||
|
|
785 | UnExSWlSSTBmeTZhZWY2cjlvQmc4ZXpKZGZma2wrZmo2ZmZmWVpUenp4UkxuV3JaVGkxcTFiMk5u | |||
|
|
786 | WjBheFoKTTA2ZlBzMnVYYnZ1KzU1RGh3NnhjT0ZDdnZycUsvMXJYYnQyNWNhTkcvbzlsdlQwZE02 | |||
|
|
787 | Y09jTkREejJFcmEwdHNiR3hwS2VuczJIRApoakl6alIwN2xwQ1FFTkxTMGdDS1RIazlhdFFvM252 | |||
|
|
788 | dlBWSlRVL0h3OENpemZVa3VYTGlnbjd1b2I5KysrbkdSbEpRVWZ2amhCN0t5CnNsaS9majBEQnc0 | |||
|
|
789 | a016T1R0TFEwbkoyZHVYTGxDcHMzYndZS3hwY2VlK3d4VnE5ZWpWS0tyS3dzTWpJeXl2eHNPcDJP | |||
|
|
790 | YnQyNjhmdnYKdnhNWEYwZHljaktob2FFbG50Qnc3dHc1WEZ4YytOLy8vVjljWFYzTjRsNGxvdkpJ | |||
|
|
791 | d1JDVjZ0NHZrVmRlZVlYRXhFVDk4K0RnWUM1ZAp1b1MzdHpjM2J0emc1WmRmTHZGOWYzMWUwcktX | |||
|
|
792 | TFZzeWZQaHdQRHc4bURadFdxbW5vaGIrcTNyRmloWGN2SG1UeHg1N0RHOXZiMTU0CjRRVUF2dnJx | |||
|
|
793 | SzFhdFdrV25UcDNvMGFNSGYvenhCd0FmZi93eHI3MzJHcjE2OWNMVDA3UEVMMGVkVHFkL2ZjU0lF | |||
|
|
794 | Zmo0K0JBUUVJQ0gKaHdkejU4N1Z0eHN4WWdUcjE2OW41TWlSUlY0cnFmMjk2N3pYaGcwYjhQRHdv | |||
|
|
795 | R3ZYcnR5NWMwZS9MbGRYVjM3ODhVZTh2THp3OFBBZwpNRENRQng1NGdEbHo1dURqNDhPb1VhTVlQ | |||
|
|
796 | SGl3ZmozdnZQTU9aOCtleGRQVGs1NDllK3BQUENqY1ptbmJ0N1cxWmZueTVZd2FOWXFCCkF3ZlNz | |||
|
|
797 | V05IMnJScFU2emRraVZMNk5peEl6NCtQcmk2dXRLalI0OFNmeS9DTXNua2cwSllxTGk0T0lZT0hj | |||
|
|
798 | cnZ2Lzl1a3UybHA2ZFQKdDI1ZFVsSlNHREprQ0o5ODhnbnQyN2MzeWJhRmVaQXhEQ0VzbUNudk9E | |||
|
|
799 | ZHYzanpDd3NLd3M3UGoyV2VmbFdKUkRja2VoaEJDQ0lQSQpHSVlRUWdpRFNNRVFRZ2hoRUNrWVFn | |||
|
|
800 | Z2hEQ0lGUXdnaGhFR2tZQWdoaERDSUZBd2hoQkFHK1g4TEF4UDFCZTVmQkFBQUFBQkpSVTVFCnJr | |||
|
|
801 | SmdnZz09Cg== | |||
|
|
802 | </png> | |||
|
|
803 | </output> | |||
|
|
804 | </outputs> | |||
|
|
805 | </codecell> | |||
|
|
806 | <markdowncell> | |||
|
|
807 | <source>## Parallel calculation of nearest neighbor eigenvalue distribution</source> | |||
|
|
808 | </markdowncell> | |||
|
|
809 | <markdowncell> | |||
|
|
810 | <source>Here we perform a parallel computation, where each process constructs and diagonalizes a subset of | |||
|
|
811 | the overall set of random matrices.</source> | |||
|
|
812 | </markdowncell> | |||
|
|
813 | <codecell> | |||
|
|
814 | <input>def parallel_diffs(rc, num, N): | |||
|
|
815 | nengines = len(rc.targets) | |||
|
|
816 | num_per_engine = num/nengines | |||
|
|
817 | print "Running with", num_per_engine, "per engine." | |||
|
|
818 | ar = rc.apply_async(ensemble_diffs, num_per_engine, N) | |||
|
|
819 | diffs = np.array(ar.get()).flatten() | |||
|
|
820 | normalized_diffs = normalize_diffs(diffs) | |||
|
|
821 | return normalized_diffs</input> | |||
|
|
822 | <language>python</language> | |||
|
|
823 | <prompt_number>11</prompt_number> | |||
|
|
824 | <collapsed>1</collapsed> | |||
|
|
825 | <outputs /> | |||
|
|
826 | </codecell> | |||
|
|
827 | <codecell> | |||
|
|
828 | <input>client = Client() | |||
|
|
829 | view = client[:] | |||
|
|
830 | view.run('rmtkernel.py') | |||
|
|
831 | view.block = False</input> | |||
|
|
832 | <language>python</language> | |||
|
|
833 | <prompt_number>12</prompt_number> | |||
|
|
834 | <collapsed>1</collapsed> | |||
|
|
835 | <outputs /> | |||
|
|
836 | </codecell> | |||
|
|
837 | <codecell> | |||
|
|
838 | <input>parallel_nmats = 40*serial_nmats | |||
|
|
839 | parallel_matsize = 50</input> | |||
|
|
840 | <language>python</language> | |||
|
|
841 | <prompt_number>13</prompt_number> | |||
|
|
842 | <collapsed>1</collapsed> | |||
|
|
843 | <outputs /> | |||
|
|
844 | </codecell> | |||
|
|
845 | <codecell> | |||
|
|
846 | <input>%timeit -r1 -n1 parallel_diffs(view, parallel_nmats, parallel_matsize)</input> | |||
|
|
847 | <language>python</language> | |||
|
|
848 | <prompt_number>14</prompt_number> | |||
|
|
849 | <collapsed>0</collapsed> | |||
|
|
850 | <outputs> | |||
|
|
851 | <output> | |||
|
|
852 | <output_type>stream</output_type> | |||
|
|
853 | <text>Running with 10000 per engine. | |||
|
|
854 | 1 loops, best of 1: 14 s per loop</text> | |||
|
|
855 | </output> | |||
|
|
856 | <output> | |||
|
|
857 | <output_type>stream</output_type> | |||
|
|
858 | <text /> | |||
|
|
859 | </output> | |||
|
|
860 | </outputs> | |||
|
|
861 | </codecell> | |||
|
|
862 | <codecell> | |||
|
|
863 | <input>pdiffs = parallel_diffs(view, parallel_nmats, parallel_matsize)</input> | |||
|
|
864 | <language>python</language> | |||
|
|
865 | <prompt_number>15</prompt_number> | |||
|
|
866 | <collapsed>0</collapsed> | |||
|
|
867 | <outputs> | |||
|
|
868 | <output> | |||
|
|
869 | <output_type>stream</output_type> | |||
|
|
870 | <text>Running with 10000 per engine.</text> | |||
|
|
871 | </output> | |||
|
|
872 | </outputs> | |||
|
|
873 | </codecell> | |||
|
|
874 | <markdowncell> | |||
|
|
875 | <source>Again, the agreement with the Wigner distribution is excellent, but now we have better | |||
|
|
876 | statistics.</source> | |||
|
|
877 | </markdowncell> | |||
|
|
878 | <codecell> | |||
|
|
879 | <input>hist_data = hist(pdiffs, bins=30, normed=True) | |||
|
|
880 | plot(s, rhos) | |||
|
|
881 | xlabel('Normalized level spacing s') | |||
|
|
882 | ylabel('Probability $P(s)$')</input> | |||
|
|
883 | <language>python</language> | |||
|
|
884 | <prompt_number>16</prompt_number> | |||
|
|
885 | <collapsed>0</collapsed> | |||
|
|
886 | <outputs> | |||
|
|
887 | <output> | |||
|
|
888 | <output_type>pyout</output_type> | |||
|
|
889 | <text>&lt;matplotlib.text.Text at 0x376c950&gt;</text> | |||
|
|
890 | <prompt_number>16</prompt_number> | |||
|
|
891 | </output> | |||
|
|
892 | <output> | |||
|
|
893 | <output_type>display_data</output_type> | |||
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|
894 | <png>aVZCT1J3MEtHZ29BQUFBTlNVaEVVZ0FBQVl3QUFBRU1DQVlBQUFEWGlZR1NBQUFBQkhOQ1NWUUlD | |||
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|
|
981 | bUVKNTJoZmoxOFB1OWxmWnBMejlESVp4RlZUODdWYmtPdzFyc3BXQmN1SGFCCnp1OTE1c3kwTXpT | |||
|
|
982 | cTI4amtQYWNzR0FCK0dvalFRdHd1S3Q3THNJK2ZvUkRPeEdiWFlSdzVjb1FmZnZpQnk1Y3ZjL2p3 | |||
|
|
983 | WVlzSHJJMisKUFBBbHd6b1BLMVVzbk5waFFOR0EzN2RxSnhGQ1dFbVZDb2FYbHhjOWUvWmsvZnIx | |||
|
|
984 | ZlBhWm5BN2o5R2RIbFdmVFA2SGZLOFdIcUlRUQpEcy9pZ25IMjdGazJiOTZNVnF0bHhvd1p0Rzd0 | |||
|
|
985 | M0pPOHY2Zi9UbHBtR3YwNjlGTTdpdjA1SGdYWnpTRGdLN1dUQ0NHc3dPS0NNVzdjCk9IeDlmVm15 | |||
|
|
986 | WkFuUFBQTU1lL2Z1cllsY0R1UDdJOTl6WCtmN2NIV3h5MnNnVmFZcHZpNmp6eHVnS1ZJN2pCQ2lt | |||
|
|
987 | cW8xNlgzbHloV2EKTm0zNjE4U3U3ZG5EcEhmdlpiMTV1Yy9MRFBZWlhPYjdUanZwYld5cndCT2hz | |||
|
|
988 | TzFGT1B4QW1lM1UvaGtLNFd4cWJOSTdOemVYbzBlUApsdmxlczJiTlRJckYxcTFiTFE3Z3lDNWN1 | |||
|
|
989 | OENCUHcvSTRhZ0thY0R3TWtUOEUvT0xqUkRDSGxWYU1PclZxMGQ2ZWpydnZmZGVtV2RGClpXWm1z | |||
|
|
990 | blhyVm1KalkybldyRm1OaExSWDY0NnRZOUNkZzZqbldrL3RLUGJ0MkZCd0tRQ2ZIOVZPSW9Tb0Jy | |||
|
|
991 | TU92Qjg1Y29SWFhubUYKYWRPbVViZHVYUjU1NUJGeWNuTEl6TXpFeTh1THlNaElGaXhZb05xaEti | |||
|
|
992 | V3NQcnFhQjdzOHFIWU0rNmU0Z09IL0lPSTErSDB3bFYzOQpMWVN3VDJZVmpMMTc5M0xod2dXeXNy | |||
|
|
993 | SjQ5OTEzR1RCZ0FIMzY5S25wYkhidGV0NTE5S2w2UHZuYkoycEhjUXlIUnNBOXM2RERKa2pwCnIz | |||
|
|
994 | WWFJVVFWbUhXV1ZMZHUzYWhidHk0dFdyUmcxcXhaN05tenA2WnoyYjM0ay9HRTNCWkNzd2JPZFJp | |||
|
|
995 | dXlwUTZzUFdsditZeWhCQ08KeUt5Q2taQ1F3SkVqUjRyL2dZc0wzdDdlTlJyS0Vhdyt1cHI3T3Qr | |||
|
|
996 | bmRnekhzbjgwTkQwRmQyeFRPNGtRb2dyTU9xMVdwOVBSdUhGagpEaDQ4U051MmJXbmF0Q2xqeG95 | |||
|
|
997 | aFY2OWU3Tnk1azVFalI5b2lheWxxblZaYldGU0kxMXd2ZGo2eEUrK21GUmRQT2EzMkZzRkx3WDhW | |||
|
|
998 | CmZQNlRzWjJjVml1RWJkWG80b01IRHg2a1M1Y3VRUEdxdEVsSlNTUW5KNU9VbE1UKy9mdkp6TXkw | |||
|
|
999 | UExFVnFGVXd0cDdheXVRZko3UDMKcWNvdldwU0NjWXM2dVJEckExK3Rnck1oU01FUXd2WlVXNjEy | |||
|
|
1000 | M3J4NVRKMDZ0VHBkVkpsYUJXUEd6ek5ZT0hjUmVUL25tUGt2cEdDWQpDSDBQT3NiRGw2dVJnaUdF | |||
|
|
1001 | N2FsV01Bb0xDODI2YVZKTlVLTmdLSXBDcC9jNmNmeGZ4K0djSFg0WTIwVzdTdHE2WnNPVWpzV0hw | |||
|
|
1002 | UzRFClNjRVF3c1pzdHJ6NXJhcFNMQXdHQTM1K2Z2ajQrTEJ3NGNKUzcrdjFlanc4UE5CcXRXaTFX | |||
|
|
1003 | djc1VC9zNXMrYndwY1BrRnVUQ09iV1QKT0xDQ0JwQXd2WGlOS1NHRXcxQmx4YndwVTZZUUZ4ZUh0 | |||
|
|
1004 | N2Mza1pHUmpCNDkydVNlM2dCOSsvWmx6Wm8xYXNTcjBPb2pxeG5XZVJpTApXS1IyRk1lMjh5bVk4 | |||
|
|
1005 | alo0VnQ1VUNHRWZMTnJET0hQbVRMVUh6TWpJQUNBaUlnSnZiMjhHRFJwRVVsSlNxWGIyZXBoQ1Rx | |||
|
|
1006 | ZTFrcnpHCmtEZ0ZuUHY2VHlFY2lrVUZZK0RBZ1F3ZVBKaVZLMWVTbjU5ZnBRR1RrNVB4OWZVMVB2 | |||
|
|
1007 | ZjM5eWN4TWRHa2pVYWpZZnYyN1FRRkJURnQKMmpST25EaFJwYkdzN1Z6V09ZNm1INlZ2Kzc1cVI2 | |||
|
|
1008 | a2Rka3dDSHpoeDJUNSt2a0tJaWxsMFNPclFvVU5zMzc2ZFpjdVc4Znp6enpOMAo2RkFtVEpoQWNI | |||
|
|
1009 | Q3dWVU1GQndkeit2UnAzTnpjK09TVFQ1Z3laUXJyMXEwcnMrM3MyYk9OWCt0ME9uUTZuVld6M0d6 | |||
|
|
1010 | dHNiVkUzUlZGCjNUcDFhMndNcDVMckFjbnc1cTl2c25Ub1VyWFRDRkZyNmZWNjlIcDk5VHRTcXVq | |||
|
|
1011 | SEgzOVUyclJwbzdScTFVcnAxYXVYa3BDUVlOYS8KdTNyMXFoSVVGR1I4UG1uU0pHWGR1blhsdGk4 | |||
|
|
1012 | cUtsSmF0V3FsNU9Ua2xIcXZHdkdyNU43bDl5cGY3UC9DT0RZb1pqeXMzVTdOc1dzZwpZNE02Q2kr | |||
|
|
1013 | ZzRGRnk4NHl5SCs3dXpXejZzeGFpTnF2cVo2ZEZoNlRTMHRKNC9mWFhDUWdJWU5teVpYejg4Y2Vj | |||
|
|
1014 | TzNlT3hZc1g4OWhqCmo1blZoNGVIQjFCOHBsUnFhaXJ4OGZHRWhZV1p0TGx3NFlKeERtUHQyclYw | |||
|
|
1015 | NjlhTmV2WFVYVUw4ZXQ1MXRwN2F5dUM3eXI1UmtxaWkKN0VMWS9SejBta1FGOVlLc3JDc3FoaFJD | |||
|
|
1016 | Z0lXSHBBWVBIc3lFQ1JQUTYvVW1aelVGQmdieTFGTlBtZDNQL1BuemlZbUpJVDgvbjlqWQpXRHc5 | |||
|
|
1017 | UFltTGl3TWdKaWFHYjc3NWhpVkxsdURxNmtxM2J0MllPM2V1SlRGcmhPR1VBVzBiTFI3MVBkU09V | |||
|
|
1018 | dnRzbnc2VC9Jb1hKN3pXClJ1MDBRb2h5V0hUaDNvNGRPd2dORGEzME5WdXg1WVY3MHpaTW8xbjla | |||
|
|
1019 | cnpTOXhYajJIWjVVWnhkdEt0Q24xRlRvTWdOZnY1UHVlMXMKOWJNV29yYXp5WVY3WmUxRnhNVEVX | |||
|
|
1020 | RHlvSTlwNGNpTUQ3eHlvZG96YWEvdHpvRjBHRFMrcG5VUUlVUTZ6RGtrbEp5ZXpZOGNPTGw2OAp5 | |||
|
|
1021 | T0xGaTQyVjZlTEZpMDV4VzliejE4NXpPdk0wUGRyMlVEdEs3WlhaRGc0K0NEM253YWJYMVU0amhD | |||
|
|
1022 | aURXWHNZR1JrWm5ENTltdno4CmZFNmZQazFhV2hwcGFXbDRlWG54MFVjZjFYUkcxVzA4dVpGNzJ0 | |||
|
|
1023 | K0RxNHNxRjhZN2oyMHZRSS8zb2I1TWNBdGhqeXlhd3poMjdCaWQKT25XcXlUd1dzZFVjeHJqdng5 | |||
|
|
1024 | SHp0cDQ4SGZLMHlkZ09NejlnODNiVjZQTnY0K0h5bldCNHBWUTdtY01Rd2pwcWRMWGE2T2hvMXE5 | |||
|
|
1025 | ZgpUL3YyN2YvNm9EUWQrT1RKa3hZUGJBMjJLQmlLb25EYk83ZGhtR0RncnVaM21ZenRjQi9HTm10 | |||
|
|
1026 | WGpUNWJISVBIZXNPQ2s1RG5idEpPCkNvWVExbEhWejA2empyRXNYNzRjZ0owN2QxbzhnS003ZFBF | |||
|
|
1027 | UTlWenJjV2V6TzlXTzRoelNPOEhKQVJDeUJINTlYdTAwUW9pYm1GVXcKbWpadENsQnFSVmxuRUg4 | |||
|
|
1028 | eW5vRWRCNWJhc3hJMWFPdExNSFpnOFZwVCtRM1ZUaU9FK0l0WkJlUENoUXRsZm1BcWlvSkdvNkZW | |||
|
|
1029 | cTFaVwpEMll2NGsvR016NXd2Tm94bk11ZlhlRjBMK2orQVNRK3EzWWFJY1JmekpyRDhQSHhLVzU4 | |||
|
|
1030 | UzlFb0tSakhqaDJybVhTVnFPazVqTHpDClBEemY5aVJsU2dvdEdyWW9OYmJEelEvWXJKMFYrdlRh | |||
|
|
1031 | QXc5SHc0SVR4VGRja2prTUlheW1SaS9jNjlTcEU3Ly8vanQ1ZVhuazUrZVQKbDVkbi9McXF5NXc3 | |||
|
|
1032 | Z29UVENYVDI3RnlxV0FnYk9LK0ZNNkhRWFZheEZjSmVtTFdIa1pHUmdZZUhCNWN1bGI0S1Y2UFIw | |||
|
|
1033 | S0tGT2grbwpOYjJIOGZLbWx5bFNpbmlqZitsYmljb2VoZzM2YkxNYlJnK0ZkNDlEUVVQWnd4RENT | |||
|
|
1034 | bXIwTEttU0ZXYWRiZEk3L21ROGIvWi9VKzBZCnp1dGNNSnp0VWJ5WFVmcW1qRUlJRzdQNDB1Vkxs | |||
|
|
1035 | eTZ4WWNNR05Cb05rWkdScXUxZDFMUXIyVmM0ZlBFd3ZXN3ZwWFlVNTdabFZ2RmUKeGk2MWd3Z2hM | |||
|
|
1036 | RnA4Y1BueTVZU0hoNU9Ra01EMjdkc0pEdzgzWHFOUjIyeEsyVVR2TzNwVHoxWGQrM0E0dlpLOURP | |||
|
|
1037 | dmUxRkVJVVFVVwo3V0g4KzkvL1p1dldyWGg1ZVFIRnA5dEdSa2J5OE1NUDEwZzROVzFNMmNqQWpy | |||
|
|
1038 | STZyVjNZOGc4WXZZYWNnaHpxdTlaWE80MFFUc3VpClBZem16WnVUbloxdGZKNmRuVTN6NXMydEhz | |||
|
|
1039 | b2V4SitJWjBESEFXckhFQURudXNNNVdMcEx6cGdTUWsxbUZZekpreWN6ZWZKa1dyWnMKU2ZmdTNi | |||
|
|
1040 | bnZ2dnNZTm13WXdjSEJ0R3paMHVKQkRRWURmbjUrK1BqNHNIRGh3bkxiSlNjbjQrcnF5cmZmZm12 | |||
|
|
1041 | eEdOV1JjaVdGYTNuWAo2TnFxcTAzSEZSWFF3MXUvdmtWT1FZN2FTWVJ3V21ZZGt1cmV2YnZ4b3Iz | |||
|
|
1042 | QmcvOTNUK3NISG5pZ1NrdG1USmt5aGJpNE9MeTl2WW1NCmpHVDA2TkdsenNBcUxDemtoUmRlSUNv | |||
|
|
1043 | cXl1YW5VOGFmTE42N2tPVkE3TWc1MExiUjh1SHVENWtVT2tudE5FSTRKYk1LeHZqeDQ2MDIKWUVa | |||
|
|
1044 | R0JnQVJFUkVBREJvMGlLU2tKS0tqbzAzYUxWeTRrQkVqUnBDY25HeTFzYzBWZnpLZUlUNURiRDZ1 | |||
|
|
1045 | cU5pc3ZyUDQyNWQvNCsvQgpmNWU1RENGVVlORWNSbloyTnF0WHIyYml4SW1NSGoyYU1XUEdNR2JN | |||
|
|
1046 | R0lzR1RFNU94dGZYMS9qYzM5K2Z4TVJFa3pabnpweGg5ZXJWClBQMTA4ZjBuYlBtWGZtRlJJWnRT | |||
|
|
1047 | TnNuOGhSM3EwYllIMmpaYS9ydjd2MnBIRWNJcFdYU1cxTXN2djR4R28ySERoZzA4Ly96ekxGKysK | |||
|
|
1048 | bkx2dnZ0dnFvWjU5OWxuZWZQTk40OVdJRlIyU21qMTd0dkZyblU2SFRxZXIxdGk3eiszR3E3RVh0 | |||
|
|
1049 | elc1clZyOWlKcFJzcGZ4ZVBEagpzcGNoaEpuMGVqMTZ2YjdhL1ZoMHg3M2c0R0IyNzk1Tmx5NWRP | |||
|
|
1050 | SGp3SUJrWkdRd1lNTUNpdzBZWkdSbm9kRHIyN05rREZFK29SMFZGCm1SeVM2dGl4bzdGSVhMcDBp | |||
|
|
1051 | WVlORzdKMDZWS0dEUnRtR3I0R2xnWjVZK3NiL0huOVQrWkh6YSt3blN3Tll2dXhTMzdXUTFZTVlm | |||
|
|
1052 | QmQKZzNrbTlCa3pzd2doYmxhaml3K1djSE56QTZCSGp4NnNXN2VPQ3hjdWtKTmoyVmtySmN1TUdB | |||
|
|
1053 | d0dVbE5UaVkrUEp5d3N6S1ROeVpNbgpTVWxKSVNVbGhSRWpSckJreVpKU3hhS21iRHk1VVE1SDJT | |||
|
|
1054 | VlhOQm9OR28yRzljK3RaOUtYazlDNGFveXZsVHlhTkttZHAza0xZUThzCk9pVDF6RFBQY09YS0Zh | |||
|
|
1055 | Wk9uY3JNbVRNNWMrWU1yNzc2cXNXRHpwOC9uNWlZR1BMejg0bU5qY1hUMDVPNHVEZ0FZbUppTE83 | |||
|
|
1056 | UFd2SUsKODloeFpnZDk3dWlqV2daUm5nS01leUpuZ1F2UkVCd055Uk5OV21WbHlabHRRdFFVaXc1 | |||
|
|
1057 | SndmL1drZ0tJakl4VWRVRkNheCtTU2ppZAp3RE0vUE1QdW1OMW1qVjJiRHZjNFhNYmJkc0NEdzR0 | |||
|
|
1058 | WHNpMnNaOUpPVnJVVm9tSTJPU1IxODFwU2lZbUo5T3JWcTFhdEpXVTRaYUNQCnQreGRPSVF6b1hD | |||
|
|
1059 | aEcyaVhxWjFFQ0tkaDBSNUdVRkFRUC8zMFU2bTFwUGJ1M1Z0akFTdGk3VDJNNkJYUlBCYjBHTVA5 | |||
|
|
1060 | aDVzMWRxMzkKNjczYTdXdzBkcGw3R2JLSElVUmxiTEtIVVp2WGtpb3NLdVRYUDM3bDdqdXNmNXF3 | |||
|
|
1061 | cUNGblFvdnYveTE3R1VMWWhGbVQzcE1uVHdZdwpyaVZWY3UzRnRtM2JHRGl3ZHF6b3V2L1AvWGcx | |||
|
|
1062 | OXFKMTQ5WnFSeEdXME0rQ0IwZkNuc2R1bWNzUVFsaGJsZGFTS3ZtNnFtdEoyYU90CnA3WVM0UjFC | |||
|
|
1063 | a3liTnljcTZvblljWWE0ellmQm5GOUIrQkR1ZlVqdU5FTFdheFdkSkFlemF0UXVOUmtOd3NMcDN0 | |||
|
|
1064 | YkhtSE1iSXIwY3kKck5Nd3hnYU5SZVlIcXR2T3htTzNTNFNSRDhLN3YwTmhmWm5ERUtJU05wbkRN | |||
|
|
1065 | QmdNZE9yVWlaZGVlb2tYWDN5UlRwMDZzWFhyVm9zSAp0VGVLb21BNFpTRENPMEx0S0tJcTBuckNu | |||
|
|
1066 | d0hRSTA3dEpFTFVhaFpkdVBmMjIyK3paczBhNCtLQlI0OGVaZnIwNmZUcDQ5aW5vdjUrCitYZnF1 | |||
|
|
1067 | OWJIdTZtMzJsRkVWVzE4RXg0ZENPcWNzQ2VFVTdCb0QrUHk1Y3UwYmR2VytMeE5telpjdm56WjZx | |||
|
|
1068 | RnN6WERLSUZkM083b0wKM2VCNEZQUldPNGdRdFpkRmV4aVBQdm9vMGRIUmpCdzVFa1ZSK1BiYmJ4 | |||
|
|
1069 | azdkbXhOWmJNWk9SeFZTMngrRFdJKzVXeldXZHE2dDYyOAp2UkRDSW1aUGVpdUt3cmx6NXpoLy9q | |||
|
|
1070 | enIxcTFEbzlFd1pNZ1F0RnB0VFdjc2w3VW12VHNzNk1BUFkzN0FyNldmaWhmazJlbUVjcFhhCnFU | |||
|
|
1071 | ajJRQTFQVEg2Q0Q0WitZRWFmUWppbnFuNTJXbFF3dW5idHlvRURCeXdlcEtaWW8yQ2N6amhOOEFm | |||
|
|
1072 | Qi9EbmpUK09LcC9KaFhOMTIKS281ZFgwUExWMXV5WmZ3Vy9GcjZtZEd2RU02bnhzK1MwbWcwaElX | |||
|
|
1073 | RnNYNzllb3NIc1dkYi95aSsvcUsyWEUvaTlITGcrZDdQOCtJdgpMNnFkUkloYXg2Sko3NlNrSklZ | |||
|
|
1074 | T0hZcVhseGRhclJhdFZxdjZ0UmpWSlJQZXRjK2swRW5zT2IrSFgvLzRWZTBvUXRRcUZrMTZyMTY5 | |||
|
|
1075 | CnV0WmRGR1U0WmVESjdrK3FIVU5ZVVgzWCtyeDJ6MnM4Ri84Y3Z6NzJxK3c5Q21FbFp1MWg1T2Zu | |||
|
|
1076 | czI3ZE9wWXVYY3JwMDZmcDJMRWoKZDkxMWwvSGhxQzVldjhpWnJETUV0ZzVVTzRxd3NvZTdQc3ox | |||
|
|
1077 | L090OGYrUjd0YU1JVVd1WVZUQmVldWtsbGl4WlFzdVdMWG4xMVZlWgpQNy9pKzExWHhtQXc0T2Zu | |||
|
|
1078 | aDQrUER3c1hMaXoxL3VyVnF3a01EQ1FvS0lqbzZHaUw3aGx1aVcxL2JLUFg3YjJvNDFLblJ2b1g2 | |||
|
|
1079 | cW5qClVvZTNCcnpGaTcrOFNFRlJnZHB4aEtnZEZETUVCd2NyZVhsNWlxSW95cFVyVjVTSWlBaHov | |||
|
|
1080 | bG01Z29LQ2xDMWJ0aWlwcWFsSzU4NmQKbFlzWEw1cThmKzNhTmVQWGVyMWU2ZE9uVDVuOW1CbS9Y | |||
|
|
1081 | TS8rOUt6eWh1R05VbjJDWXNaRHJYYVNzYkoySllxS2lwUituL1JUM2s5Kwp2MXIvUHhHaXRxbnFa | |||
|
|
1082 | NmRaZXhoRlJVVzR1YmtCMExScFV6SXpNNnRjb0RJeU1nQ0lpSWpBMjl1YlFZTUdrWlNVWk5LbVVh | |||
|
|
1083 | TkdKdTNyCjE2OWY1ZkVxc3ZYVVZybkRYaTJtMFdoNGU4RGJ6Tmt5aCt0NTE5V09JNFRETTJ2U2U5 | |||
|
|
1084 | KytmYmk3dXh1ZloyZG5HNTlyTkJxTENraHkKY3JKeExTb0FmMzkvRWhNVGlZNk9ObW4zM1hmZk1Y | |||
|
|
1085 | WHFWSzVkdThhdVhidks3Vy8yN05uR3IzVTZIVHFkenF3Y21ibVpITGwwaEpDMgpJV1puRjQ3QXRm | |||
|
|
1086 | UWs5M0JvdktZeEdFeGZkbmR2Um1hbTR5OXRJMFJsOUhvOWVyMisydjJZVlRBS0N3dXJQWkNsN3Iv | |||
|
|
1087 | L2Z1Ni8vMzYrCit1b3IvdmEzdjdGbno1NHkyOTFjTUN5eC9mUjJ1cmZ0VGoxWHVlbE83VkpBcVF2 | |||
|
|
1088 | OE5wMkVKMEpoMXlHNDNzcjRjbGFXbkQwbG5NT3QKZjB6UG1UT25TdjFZZEIyR05ZU0VoSERreUJI | |||
|
|
1089 | ajg0TUhEOUt6Wjg5eTI0OGFOWXF6WjgrYTNCcldHa291MkJOTzRFcEgyUGN3NkdhcgpuVVFJaDJi | |||
|
|
1090 | aTNkZEpBQUFjV1VsRVFWVHpndUhoNFFFVW55bVZtcHBLZkh3OFlXRmhKbTFPbkRoaHZON2poeDkr | |||
|
|
1091 | b0h2MzdqUm8wTUNxCk9ReW5ERVRjSVFYRGFXeVpCWDdmUXB2ZGFpY1J3bUZaZE9HZXRjeWZQNStZ | |||
|
|
1092 | bUJqeTgvT0pqWTNGMDlPVHVMamltOS9FeE1Td2F0VXEKUHYzMFU5emMzTkJxdGJ6OTl0dFdIVCtu | |||
|
|
1093 | SUljOTUvWVFmbnU0VmZzVmRpeTdPZnp5QnR6N0RDejdGY3c3MzBNSWNaTXEzYUxWWGxSMQpBUzNE | |||
|
|
1094 | S1FNemZwN0JqaWQybE5tbkxPeFgzWFpxamwxQk8wMFJQTlliZGo4QmV4NERySGVMWHlFY2lVMXUw | |||
|
|
1095 | VnBiR0U0WjVIUmFaNlM0CndBK0xvUDlMMEVET2poTENVazVaTUxiK3NWWG1MNXpWdVdBNE5BTDZ2 | |||
|
|
1096 | YXgyRWlFY2p0TVZqSUtpQWhKT0ozRDNIWGVySFVXb1pkTnIKNFBzZHRGRTdpQkNPeGVrS3hwNXpl | |||
|
|
1097 | N2pENHc1YU5HeWhkaFNobHB4bThNdS9JQnFLbENLMTB3amhNSnl1WUNTa0pkRDdqdDVxeHhCcQor | |||
|
|
1098 | MjBzRk1HeVBjdlVUaUtFdzNES2doSGVUazZuZFhxS0Mvd0EvN2ZwLzBpL2thNTJHaUVjZ3RNVmpN | |||
|
|
1099 | UzBSSHEySy8vS2N1RkV6c09vCkxxUDR2MDMvcDNZU0lSeUNVeFdNODlmT2s1R1RRYWNXbmRTT0l1 | |||
|
|
1100 | ekVxL2U4eXVxanEwaytVelAzWEJHaU5uR3FncEdVbGtSWXV6QmMKTkU3MWJZc0tOSzNmbExjR3ZN | |||
|
|
1101 | WEVIeVpTV0dUN1JUYUZjQ1JPOWNtWmtKWkF6OXZrY0pRdzlXaTNSMm5vMXBCM2s5NVZPNG9RZHMy | |||
|
|
1102 | cApDb2JNWDRpeWFEUWFsZzFieHV0YlgrZm9wYU5xeHhIQ2JqbE53U2dvS21EWHVWMkUzaGFxZGhS | |||
|
|
1103 | aGgrNXNmaWR6ZEhNWXYzcThISm9TCm9oeE9VVENhTkdtTzIyMXVYRHR6amVZTm02UFJhTXA5Q09m | |||
|
|
1104 | MWRNalROSEJ0d055RXVXcEhFY0l1T1VYQnlNcTZBdTBXUTlvRWlsY3kKcmVnaG5KV0x4b1ZsOXkz | |||
|
|
1105 | ajM5di96Y0UvRDZvZFJ3aTc0eFFGQTRCMmlaQW04eGVpWXUyYnR1ZjFmcTh6N3Z0eDVCZm1xeDFI | |||
|
|
1106 | Q0x1aQpTc0V3R0F6NCtmbmg0K1BEd29VTFM3Mi9mUGx5QWdNRENRd01aTXlZTVJ3N2RxejZnMHJC | |||
|
|
1107 | RUdaNkl2Z0pXalJzd1Z1L3ZxVjJGQ0hzCmlpbzNVTkpxdFN4WXNBQnZiMjhpSXlQWnRtMGJucDZl | |||
|
|
1108 | eHZjVEVoTHc5L2ZIdzhPRFR6NzVoSTBiTi9MWlo1K1Y2c2ZjbTRCb0dtcmcKV1hkNDh3b29kU3By | |||
|
|
1109 | amQzZCtNZHV4cTV0R2QyQWdyTGZhZ0xFQUorQys0MW1aR2JLL1RORTdlRXdOMURLeU1nQUlDSWlB | |||
|
|
1110 | bTl2YndZTgpHa1JTVXBKSm0vRHdjT085djZPam85bXlaVXYxQm0wSG5Ba3hvMWdJNTFKQXVYTlpt | |||
|
|
1111 | UXJFZndUM0I1SjE0NHFhSVlXd0d6WXZHTW5KCnlmajYraHFmKy92N2s1aVlXRzc3RHo3NGdLRkRo | |||
|
|
1112 | MVp2MEhiSTRTaGh1YjNqSU9OMmtIdHRDUUdBcTlvQktySng0MFkrLy94enRtL2YKWG02YjJiTm5H | |||
|
|
1113 | Ny9XNlhUb2RMclNqZG9CU2JKQ3JiQ1VCdForQURGdDBhZnEwYlhYcVIxSWlDclI2L1hvOWZwcTky | |||
|
|
1114 | UHpPWXlNakF4MApPaDE3OXV3QllQTGt5VVJGUlJFZEhXM1NidCsrZlR6d3dBUDg5Tk5QM0hYWFhX | |||
|
|
1115 | WDJaYzV4dUNLbGlEb3YxWUYzLzRRYkxjMUk2QWpICjNpVmo5ZHRaMExhamhqWVQyN0RyeVYyMGNa | |||
|
|
1116 | ZmI5QW5INXpCekdDVnpFd2FEZ2RUVVZPTGo0d2tMQ3pOcDg4Y2ZmekI4K0hDV0wxOWUKYnJFdzEr | |||
|
|
1117 | R0xoK0VHWmhZTEljcHdFbUs2eHpCNjFXZ0tpc3FaSkJmQ0NhaHlXdTM4K2ZPSmlZbGh3SUFCVEp3 | |||
|
|
1118 | NEVVOVBUK0xpNG9pTAppd1BnMVZkZjVmTGx5enoxMUZOb3RWcENRNnUrbkVkaVdpS2tXU3U1Y0ZZ | |||
|
|
1119 | dlI3eE0zVHAxZVdYeksycEhFVUkxcXB4V2F5M203Rlk5CnNmWUpQbnp0UTBoMndrTXBrdEZxZlNx | |||
|
|
1120 | S3dzWHJGK24rUVhjV1J5OW1TS2NoWm80aGhQMXhtRU5TdHBad09rSDJNSVJWdEd6VWtpOUgKZk1u | |||
|
|
1121 | amF4NG45V3FxMm5HRXNMbGFYVEF5Y2pLS2Y3RXZxSjFFMUJhOWJ1L0ZDNzFmWU9UWEk4a3R5RlU3 | |||
|
|
1122 | amhBMlZhc0xSdkxaWklMYgpCRU9SMmtsRWJUSzE1MVJ1YjNJNzAzNmVwbllVSVd5cVZoZU1oTk1K | |||
|
|
1123 | Y3NNa1lRV3VKa3ZndTdpNDhOMjQ3MWk4WVRHYXdQKzkzcVJKCmM3V0RDbEdqYW5YQlNEd2pkOWdU | |||
|
|
1124 | MWxER0VpSzVDbnk1RHlKYmd2Y1dRQ2xlUmwrSVdxeldGZ3hGVWVTV3JLSm0vZGtWVmkySGtTT2gK | |||
|
|
1125 | NVNHMTB3aFI0MnB0d1RoKytUaU42emFtclh0YnRhT0kydXprUUloL0d4NitGeHFySFVhSW1sVnJD | |||
|
|
1126 | MFpDbXN4ZkNCdjViUnpzZmh3ZQpocXpjTExYVENGRmphbTNCU0V4THBPZHRVakNFalJoZWhyUHc0 | |||
|
|
1127 | RGNQeXAzNlJLMVZ1d3VHN0dFSW05SEFldENnNGVuMVQxZnBLbG9oCjdGMnRMQmpYODY1ek5QMW84 | |||
|
|
1128 | VFVZUXRoS0Vhd2N1Wkk5NS9md1Q4TS8xVTRqaE5YVnlvS3g4K3hPdXJicVNqM1hlbXBIRVU2bWNk | |||
|
|
1129 | M0cKckIrem5tVjdsN0ZveHlLMTR3aGhWWFo5QTZXcWtzTlJRazFlamIzWU5IWVQvVDd0UjE1aEhs | |||
|
|
1130 | UERwNm9kU1FpcnFKVjdHSWxuRWdsdgpKM2ZZRStycDBLd0RXOFp2WWZIT3hmeHI2Ny9VamlPRVZk | |||
|
|
1131 | UzZnaUVYN0FsN2NZZkhIV3dadjRWUDkzM0tMUDBzbVFnWERxL1dGWXhUCkdhZUE0bDlXSWRUVzFy | |||
|
|
1132 | MHQrbkY2dmozOExTLys4cUlVRGVIUVZDa1lCb01CUHo4L2ZIeDhXTGh3WWFuM2p4dzVRbmg0T1BY | |||
|
|
1133 | cjEyZnUKM0xrVzlWMnlkNkhSYUt3VlY0aHFhZDI0Tlp2SGJXYkRpUTFNKzNtYUZBM2hzRlFwR0ZP | |||
|
|
1134 | bVRDRXVMbzZOR3pleWFORWlMbDI2WlBKKwppeFl0V0xod0lUTm16TEM0NzhRMG1iOFE5c2V6b1Nl | |||
|
|
1135 | YnhtN2kxejkrWmVJUEV5a3NLbFE3a2hBV3MzbkJ5TWpJQUNBaUlnSnZiMjhHCkRScEVVbEtTU1p1 | |||
|
|
1136 | V0xWdlNvMGNQM056Y0xPNWZsZ1FSNmpGZEJ2M1dSL09HelVtZW5NelNWUjh5NUlzaFhNMjVxblpn | |||
|
|
1137 | SVN4aTg0S1IKbkp5TXI2K3Y4Ym0vdnorSmlZbFc2VHVuSUljRGZ4NmdlNXZ1VnVsUENNdVVzUXg2 | |||
|
|
1138 | R2N1aUYzNVN3RjNON3lMc3d6Q09YanFxWGx3aApMT1R3MTJITW5qM2IrSFZMLzViNGV2clNxRzRq | |||
|
|
1139 | OVFJSlVaa2lXRGg0SVV0M0xhWFBSMzM0NUcrZk1OaG5zTnFwUkMybTErdlI2L1hWCjdzZm1CU01r | |||
|
|
1140 | SklUbm5udk8rUHpnd1lORVJVVlZ1YitiQzhhOGhIbjBiQ3lIbzRSamVLTDdFL2kxOUdQazF5T1pI | |||
|
|
1141 | ajZkNmVIVDVXUU4KVVNOME9oMDZuYzc0Zk02Y09WWHF4K2FIcER3OFBJRGlNNlZTVTFPSmo0OG5M | |||
|
|
1142 | Q3lzekxhV25rMlNrSllnSzlRS2gzTDNIWGVUOVBjawpWdXhmd2Rqdng1SlRrS04ySkNIS3BWRlVP | |||
|
|
1143 | TWR2eTVZdFBQWFVVK1RuNXhNYkcwdHNiQ3h4Y1hFQXhNVEVjUDc4ZVVKQ1Fzak16TVRGCnhRVjNk | |||
|
|
1144 | M2NPSFRwRTQ4YW1kNmpSYURRbVJlV09lWGZ3eTloZjhHbmhVNnBkOFRGa2M1amJWcTEyYW80dEdh | |||
|
|
1145 | M1Y3dFpmdXh2NU4zaHMKOVdNY1N6L0dwL2QvU2tDckFEUDZFYUpxYnYzc05QdmZxVkV3ck9YbWIv | |||
|
|
1146 | cE01aGtDM3cvazRuTVhTKzNXUzhHd2RUczF4M2FNakdYOQoyaW1Ld29lN1ArU2xUUzh4STN3R00z | |||
|
|
1147 | ck5vSTVMSFRQNkU4SXlUbHN3alB3QUxiQ2l2TmIyL3lFaUdXM1pUczJ4Sy81bFRiMmF5b1RWCkU4 | |||
|
|
1148 | Z3R5T1hqdjMxTXB4YWR6T2hUQ1BOVnRXRFVncVZCL2pwZHNkME1TSHZ0Zjg5TkhrSTRqdlpOMi9Q | |||
|
|
1149 | TDJGOTRLT0FoZXYyM0YrOG0KdlV1UlVxUjJMQ0ZxUThINFM3dEVTSk1KYjFFN3VHaGNpQTJMWmZ2 | |||
|
|
1150 | ajIvbnl3SmYwLzdRL3h5OGZWenVXY0hLMW8yQzQ1RU9iUFhBbQpWTzBrUWxoVnB4YWQyRHBoSzlF | |||
|
|
1151 | KzBmVDhzQ2RUTjB6bGN2Wmx0V01KSjFVN0NrYnJmWENsQStRMlVUdUpFR2FvZUFtUm14OU5talNu | |||
|
|
1152 | Cmprc2RadlNhd2NHSkI4a3R5S1h6ZTUxNUorRWRjZ3R5MWY1R2hKT3BIUVhqOWdRNUhDVWNpQmxM | |||
|
|
1153 | aVB6MXlNcTZZdnhYclJ1M1puSDAKWWd6akRlaFQ5Zmd0OG1QbHdaV3krcTJ3bVZwd2xwUUNEendD | |||
|
|
1154 | S2YxZ3oyUGx0Y1FSenB5UmpMWnNwK2JZbG1VczcxZDBjOHBtWnNUUAp3TTNGamYvcjgzOUVkNHJH | |||
|
|
1155 | UlZNNy9nWVVOY3VKVDZ0VklQWXUrR0lOWFBRdnJ5VzE3VU5FTWxhM25acGpXNmRnQUJRcFJhdzh1 | |||
|
|
1156 | SkovCmIvODMxL091TTdYblZCNE5mSlNHYmczTjdGODRvNm9XRElkZmZKQkdmMExEUzNESnQvSzJR | |||
|
|
1157 | amdjVjdQV2wycnMzcFIxKzc1bmJzSmMKWHRuOENrLzFlSXBuUXA2aGRlUFdOc2dvbklYajc3L2Vs | |||
|
|
1158 | bFI4ZHBUaStOK0tFS1daTjk5eExlc3FmZHYzWmMzb05XeWRzSldMTnk3aQp1OGlYOGQrUFoxUEtK | |||
|
|
1159 | cmxoazdBS3h6OGsxZThsS0hJRmZVV3JMOWErd3hTU3NicnQxQnk3WmpMZStxdDg2Y1lsUHQ3N01W | |||
|
|
1160 | OGMrSUp6CldlY1lGVENLTVFGajZORzJoNnlLNitTY2R3NWpiRC9ZUGdPT1YzUS9BZWY5RUpHTTlq | |||
|
|
1161 | aTJiUXJHelk1Y09zSVhCNzVneGY3aXRYUEcKZEIzRDZJRFIrSHJLb1Z4bjVMd0Y0MFYzbUo4SzJj | |||
|
|
1162 | MHJhb216Zm9oSVJuc2MyL1lGbzRTaUtPdzh1NU12RG56Qmx3ZStwRW05Smd6bwpPSUQrSGZxamE2 | |||
|
|
1163 | K2pXWU5tWnVZU2pzeDVDOGFrenZEZWtjcGE0cXdmSXBMUkhzZXVpWXh1Rk05M1ZNemR2Um1abWNW | |||
|
|
1164 | WGloY3BSZngyCi9qYzJudHpJTHltLzhPdnBYL0h6OURNV2tONTM5S2ErYTMwemN3cEg0cndGNDIv | |||
|
|
1165 | ajRQdVBLMnVKYzM2SVNFYjdIRnZkak9YOXl1Y1cKNUpLWWxzakdsSTM4Y3ZJWDl2KzVuKzV0dWhQ | |||
|
|
1166 | Y0pwZ2dyeUNDdklMdzgvVERyWTZibWRtRnZYTGVndEZqQ2V4OHFyS1d5SWVJTGR1cApPYlprckt5 | |||
|
|
1167 | ZHViL3ltYm1aSkp4T1lPLzV2ZXk5c0plOTUvZHk2dW9wZkQxOWpRVWt5Q3VJd05hQmVOVDNNS3RQ | |||
|
|
1168 | WVI4Y3FtQVlEQVppClltSW9LQ2dnTmphV3laTW5sMnJ6NG9zdjh0VlhYOUdzV1RPV0wxK09yMi9w | |||
|
|
1169 | eVRtTlJnTmVlK0I4VUNVanF2MGhzaG5RV2FrL1M4ZTIKcEoyZWluUGFRMGFvT0tka3JKamxoNjV1 | |||
|
|
1170 | ZGozdk9nZitQR0JTUlBaZjJFOTkxL3A0Ti9XbWZkUDJ4UStQNHYrV3ZOYWtYdlhXZWRQcgo5U2Iz | |||
|
|
1171 | cExaWGpwTFRvUzdjbXpKbENuRnhjWGg3ZXhNWkdjbm8wYVB4OVBRMHZyOWp4dzYyYnQzS3pwMDcy | |||
|
|
1172 | YkJoQXpObXpHRGR1blZsCmQvYW5JOXpLVWsvbEJjTWU2SkdjMXFMSFBqT1dYTmRSWXZaZkQxTlpX | |||
|
|
1173 | V1dmZHR1b2JpUEMyb1VSMWk3TStKcWlLRnk4Y1pIVXE2bWMKdW5xSzFLdXBIRWsvd2s4bmZqSytW | |||
|
|
1174 | cmRPWGRvM2JjL3RIcmZqMmRDVEZnMWEwS0pCQzVvM2FFNkxocVcvcnVkYXoyUmNSL2tnZHBTYwpW | |||
|
|
1175 | V1h6Z3BHUmtRRkFSRVFFQUlNR0RTSXBLWW5vNkdoam02U2tKRWFNR0VIejVzMFpQWG8wTDcvOGN2 | |||
|
|
1176 | a2RGam4reGVwQzJCL3pyakF2CjVnYmtWOWppZXNQcjVMWXQ0T1UxTDVOK0k1MzA3T0xIc2N2SFNM | |||
|
|
1177 | K1J6dVhzeThXdi9mVzFXeDIzNHFMU3NMaVFuRGx3aHFPcmpsTGYKdFQ0TlhCdlF3SzFCOFg5di92 | |||
|
|
1178 | cXYvOVozclYvcXRaTC8xbk90UngxTkhlcTQxTUZGNDBJZHpWLy8vZXU1cUpqTlAyMlRrNU5ORGkv | |||
|
|
1179 | NQorL3VUbUpob1VqQjI3TmpCbzQ4K2FuemVzbVZMVHB3NHdaMTMzbW5UckVJNHIxdjNSQ3BpeHVH | |||
|
|
1180 | d0czRGp1QnZEL1lkWDJsdGo5NmFjCnZmZ0g2ZGwvRlpJYjZmeDN4MzhaMm1rbzJmblpaQmRrazUy | |||
|
|
1181 | ZlRVNUJEdGZ6cjNNcCs1TEo2emUvZit0cnVZVzVGQllWVXFRVVVhajgKOWQraVFncVY0aXZoeXlv | |||
|
|
1182 | aVpUMHY3NzMwNUhTK1hmSnRtVzFkTkM1b0tDN0NOeGRqdFY2ckNydjg4MXhSbEZMSDE4ci9SczNk | |||
|
|
1183 | QUpacwpLR3YzT2Vldmg3WDZzNlN0cGUwcXkya1BHYUhpbkpMUnNuYmw1YXlKakpXN2xuV1ZKdlZM | |||
|
|
1184 | ejNsOHRlZ3JxNDFSbnFLLy9sY2RGMys0CmFLVTA5c2ZtQlNNa0pJVG5ubnZPK1B6Z3dZTkVSVVda | |||
|
|
1185 | dEFrTEMrUFFvVU5FUmtZQ2NQSGlSVHAyN0ZpcUx3Yyt3VXNJSVJ5T3pRL2EKZVhnVW4zNW5NQmhJ | |||
|
|
1186 | VFUwbFBqNmVzTEF3a3paaFlXR3NXcldLOVBSMFZxeFlnWitmbjYxakNpR0V1SVVxaDZUbXo1OVBU | |||
|
|
1187 | RXdNK2ZuNQp4TWJHNHVucFNWeGNIQUF4TVRHRWhvWnk5OTEzMDZOSEQ1bzNiODdubjMrdVJrd2ho | |||
|
|
1188 | QkEzVSt6Y2xpMWJGRjlmWCtXdXUrNVMzbjMzCjNUTGJ6Snc1VStuUW9ZTVNIQnlzSEQ1ODJNWUpp | |||
|
|
1189 | MVdXYy9QbXpVcVRKazJVb0tBZ0pTZ29TSG50dGRkc25uSENoQWxLcTFhdGxJQ0EKZ0hMYjJNTzJy | |||
|
|
1190 | Q3luUFd4TFJWR1VQLzc0UTlIcGRJcS92Ny9TdDI5Zlpmbnk1V1cyVTN1Ym1wTlQ3VzJhbloydGhJ | |||
|
|
1191 | YUdLb0dCZ1VwWQpXSmp5emp2dmxObE83VzFwVGs2MXQrWE5DZ29LbEtDZ0lHWElrQ0Zsdm0vcDly | |||
|
|
1192 | VDdnaEVVRktSczJiSkZTVTFOVlRwMzdxeGN2SGpSCjVQMmtwQ1NsZCsvZVNucDZ1ckppeFFvbE9q | |||
|
|
1193 | cmFMbk51M3J4WkdUcDBxQ3JaU2hnTUJtWDM3dDNsZmhEYnk3YXNMS2M5YkV0RlVaUnoKNTg0cGUv | |||
|
|
1194 | YnNVUlJGVVM1ZXZLaDA2TkJCeWN6TU5HbGpEOXZVbkp6MnNFMnZYNyt1S0lxaTVPVGtLRjI2ZEZG | |||
|
|
1195 | Ky8vMTNrL2Z0WVZzcQpTdVU1N1dGYmxwZzdkNjR5WnN5WU12TlVaWHZhOVluSE4xK3o0ZTN0YmJ4 | |||
|
|
1196 | bTQyYTNYck54K1BCaHU4d0o2ay9TOStuVGgyYk55bCtOCjFCNjJKVlNlRTlUZmxnQmVYbDRFQlJX | |||
|
|
1197 | dk11RHA2VW1YTGwzWXVYT25TUnQ3MktibTVBVDF0Mm5EaHNXM2xiMTI3Um9GQlFYVXEyZDYKOFo0 | |||
|
|
1198 | OWJFdW9QQ2VvdnkwQjB0TFMrT0dISC9qNzMvOWVacDZxYkUrN0xoamxYYk54c3gwN2R1RHYvNzk3 | |||
|
|
1199 | ZVpkY3MyRkw1dVRVYURScwozNzZkb0tBZ3BrMmJadk9NNXJDSGJXa09lOXlXeDQ4ZjUrREJnNFNH | |||
|
|
1200 | aHBxOGJtL2J0THljOXJCTmk0cUtDQXdNcEhYcjFreWFOSW5iCmI3L2Q1SDE3MlphVjViU0hiUWt3 | |||
|
|
1201 | ZGVwVS92M3ZmK1BpVXZiSGZGVzJwMTBYREhNb0ZsMnpvWjdnNEdCT256NU5jbkl5L3Y3K1RKa3kK | |||
|
|
1202 | UmUxSXBjaTJySnFzckN4R2pSckZ2SG56YU5Tb2tjbDc5clJOSzhwcEQ5dlV4Y1dGMzM3N2plUEhq | |||
|
|
1203 | N040OFdMMjdObGo4cjY5Yk12SwpjdHJEdGx5M2JoMnRXclZDcTlXV3U3ZFRsZTFwMXdVakpDU0VJ | |||
|
|
1204 | MGYrZDYrTGd3Y1Awck5uVDVNMkpkZHNsQ2p2bW8yYVpFNU9kM2QzCkdqWnNpSnViRzQ4Ly9qakp5 | |||
|
|
1205 | Y25rNXViYU5HZGw3R0ZibXNPZXRtVitmajdEaHcvbjBVY2Y1Yjc3N2l2MXZyMXMwOHB5MnRNMmJk | |||
|
|
1206 | KysKUGZmZWUyK3B3N3Iyc2kxTGxKZlRIcmJsOXUzYldiTm1EUjA2ZEdEMDZORnMyclNKc1dQSG1y | |||
|
|
1207 | U3B5dmEwNjRMaEtOZHNtSlB6d29VTAp4bXErZHUxYXVuWHJWdWF4VHpYWnc3WTBoNzFzUzBWUmVQ | |||
|
|
1208 | enh4d2tJQ09EWlo1OHRzNDA5YkZOemNxcTlUUzlkdXNUVnExY0JTRTlQCjUrZWZmeTVWMk94aFc1 | |||
|
|
1209 | cVRVKzF0Q2ZER0cyOXcrdlJwVWxKUytQTExMK25YcngrZmZ2cXBTWnVxYkUrN1hCcmtabzV5elVa | |||
|
|
1210 | bE9iLzUKNWh1V0xGbUNxNnNyM2JwMVkrN2N1VGJQT0hyMGFMWnMyY0tsUzVlNC9mYmJtVE5uRHZu | |||
|
|
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1213 | ZlFFa0lJWVR0MlBVaEtTR0VFUFpEQ29ZUVFnaXpTTUVRUWdoaEZpa1lRZ2doekNJRlExaVZpNHNM | |||
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1220 | L2RIcTlXeVljT0dTci92N094czNubm5IZnIyN1V0MGREUjZ2UjZBOFBCd2s2dHBTL1orCmNuSnl5 | |||
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1221 | bXhmbnRPblR6TjQ4R0NDZ29JSURBemt4SWtUcEthbTR1L3Z6K09QUDQ2Zm54OXo1c3d4NW4vdHRk | |||
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1222 | Y0lEUTBsSkNTRU45NTQKdzZTZjZkT25vOVZxNmQ2OU95a3BLYVNtcHRLMWExY0FQdjc0WXg1NjZD | |||
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1224 | RkJRUVFIQjNQdDJyVkt0NTF3SU5WWk9sZUlXelZ1M0ZqSnpNeFUycmR2cjJSa1pDai8rYzkvCmxO | |||
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1225 | bXpaeXVLb2loVHAwNVYzbjc3YlVWUkZPV05OOTVRbm4vK2VVVlJGR1hjdUhGSzM3NTlqVXR1ang4 | |||
|
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1226 | L1hoazhlTENTbTV1cmZQengKeDByanhvMFZ2VjZ2NU9ibUtuNStmc2FsNHk5ZnZxd29pcUxrNXVZ | |||
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1227 | cVlXRmh5clZyMXhSRlVSU2RUcWZzMnJWTFVSUkZhZCsrdlpLZQpubTdNbDUrZnIvVHAwMGRadDI2 | |||
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1228 | ZHNlM1ZxMWNWUlZHVTU1OS9Ydm55eXk4VlJWR1VidDI2S1VsSlNjcTFhOWVVcUtpb01wZUgzcng1 | |||
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1229 | CnMvRStBeDk5OUpHeVlNRUNSVkVVNWZ6NTgwcG9hS2lpS0lveWI5NDhaZGFzV1lxaUtNclpzMmVW | |||
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1230 | enAwN1Y5ais1ajV2Tm12V0xPWEQKRHo4MGZnL1oyZGxLU2txS290Rm9sRysvL1ZiSnljbFJIbmpn | |||
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1231 | QWVXYmI3NHgyVFlGQlFYSzBLRkRsU05IamhpMzlhSkZpNHpiN2NhTgpHMHBLU29weEtmbVBQdnBJ | |||
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1232 | YWRXcWxYTDI3RmtsTXpOVGFkZXVuWktYbDZmazUrY3I3ZHUzVjFKU1VwVDA5SFFsT0RoWW1UeDVj | |||
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1233 | cW1jCjQ4YU5Velp1M0tnb1N2RXk0QVVGQmFYYUNNY2xleGpDNnR6ZDNSazdkcXpKWDZjQVAvNzRJ | |||
|
|
1234 | NDg5OWhnQWp6LytPR3ZYcmdXS0R5R04KR0RFQ2QzZDNZOXNSSTBaUXQyNWR3c1BEYWRxMEtYMzc5 | |||
|
|
1235 | cVZ1M2Jwb3RWcmpTc0R4OGZGRVIwZWoxV281ZWZJa216WnRxalJiYkd3cwovZnYzSnpvNm1sMjdk | |||
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1236 | bkhnd0FGME9oMWFyWloxNjlaaE1CZzRjK1lNaXFJUUdocEtvMGFOR0RWcVZLWExWYTlhdFlxbFM1 | |||
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1237 | ZWkxV3FKCmlvcml3b1VMcEtTazhPQ0REL0xOTjk4QXNITGxTdU5jUVZudFQ1NDhXVzcvSVNFaHpK | |||
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1238 | OC9uN2ZlZW92TGx5OVR2MzU5b0hoWm12dnYKdjU5Njllb3hldlJvZnZycEp3QjI3dHpKOE9IRDZk | |||
|
|
1239 | YXRHN3QzNytibm4zOG1MeStQelpzMzg4UVRUd0RGaHc4Yk5HaFFhcXhCZ3diUgpwazBiM04zZDhm | |||
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1240 | ZjNaL2Z1M1NRbUp0SzFhMWZhdDI5UDgrYk5HVFpzV0puYkpEdzhuSmt6Wi9MZWUrOVJVRkJBblRw | |||
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1241 | MUt2MlpDTWRoCjkwdURDTWYwN0xQUEVod2N6SVFKRTB4ZUwrK0R0MDJiTmliUFM5Ym5xbHUzTGsy | |||
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1242 | Yk5qVytYcmR1WGZMeThzakt5bUxtekpsczNicVYKMjI2N2pmdnZ2NThyVjY1VW1Pbmpqei9tOU9u | |||
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1243 | VExGNjhHQ2hlcGpvZ0lJRE5temVidEV0TFN6UHZtN3hKVVZFUml4WXRJaUlpb3RSNwpMVnEwWVAv | |||
|
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1244 | Ky9heGN1ZEs0TkVONTdVdVc2N2hWZEhRMDNidDM1L1BQUDZkMzc5NTgvZlhYSnR1bFJNbjh6ZVRK | |||
|
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1245 | ay9ubW0yOElDQWhnCjZ0U3BYTGx5QlkxR1UrWUtwYmU2ZFh2bjVPVGc2dXBxTWpkVVhoOHhNVEVN | |||
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1246 | SERqUXVCUkpVbElTclZ1M3JuQTg0VGhrRDBQVWlHYk4KbXZIZ2d3L3kzLy8rMS9oQmMrKzk5L0xK | |||
|
|
1247 | SjU5UVZGVEVzbVhMR0Rac1dKWDZWaFNGcTFldjR1Ym1ocGVYRjhlT0hlT1hYMzZwOE4vcwoycldM | |||
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1248 | dVhQbjh0bG5ueGxmQ3drSjRjS0ZDOFk5bHV2WHIvUDc3Ny9UcmwwNzZ0U3BRM0p5TXRldlgyZmx5 | |||
|
|
1249 | cFdWWmhvelpneHhjWEZrClpXVUJtQ3g1UFdyVUtONTY2eTB5TXpNSkNBaW90SDFaVWxKU2pHc1g5 | |||
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1250 | ZS9mM3pndmtwR1J3ZmZmZjA5dWJpNWZmZlVWVVZGUjVPVGsKa0pXVlJmdjI3VGx6NWd5clY2OEdp | |||
|
|
1251 | dWVYN3JubkhwWXVYWXFpS09UbTVwS2RuVjNwOTZiUmFPalpzeWY3OSs4bk5UV1Z5NWN2czI3ZAp1 | |||
|
|
1252 | akpQYURoeDRnUWRPM2JrSC8vNEI3Nit2blp4cnhKaFBWSXdoRlhkL0NFeWZmcDBMbDI2Wkh3K1k4 | |||
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1253 | WU0vdmpqRDdSYUxSY3VYR0RhCnRHbGwvcnRibjVmMTN1MjMzODd3NGNNSkNBaGcwcVJKNVo2S1d2 | |||
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1254 | Slg5YUpGaTdoeTVRcjMzSE1QV3EyV0o1OThFb0RQUHZ1TUpVdVcKMEsxYk4zcjE2c1hSbzBjQitP | |||
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1255 | Q0REM2p4eFJlNSsrNjdDUXdNTFBQRFVhUFJHRjhmTVdJRW9hR2hSRVpHRWhBUXdLeFpzNHp0Um93 | |||
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1256 | WQp3VmRmZmNXRER6NW84bHBaN1cvdTgyWXJWNjRrSUNDQWtKQVFidHk0WWV6TDE5ZVhOV3ZXRUJR | |||
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1257 | VVJFQkFBTkhSMGRTdlg1K1pNMmNTCkdocktxRkdqdVBmZWU0Mzl2UDc2Nnh3L2ZwekF3RUI2OSs1 | |||
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1258 | dFBQR2daTXp5eHE5VHB3N3Z2ZmNlbzBhTklpb3FpcTVkdTlLaFE0ZFMKN1JZc1dFRFhybDBKRFEz | |||
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1259 | RjE5ZVhYcjE2bGZsekVZNUpGaDhVd2tHbHBxWXlkT2hROXUvZmI1UHhybCsvVHFOR2pjakl5R0RJ | |||
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1260 | a0NGOAorT0dIZE83YzJTWmpDL3NnY3hoQ09EQmIzbkZ1OXV6WmJOeTRFVGMzTng1NTVCRXBGazVJ | |||
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1261 | OWpDRUVFS1lSZVl3aEJCQ21FVUtoaEJDCkNMTkl3UkJDQ0dFV0tSaENDQ0hNSWdWRENDR0VXYVJn | |||
|
|
1262 | Q0NHRU1Ndi9BMkdrMllXcWd3ZTFBQUFBQUVsRlRrU3VRbUNDCg== | |||
|
|
1263 | </png> | |||
|
|
1264 | </output> | |||
|
|
1265 | </outputs> | |||
|
|
1266 | </codecell> | |||
|
|
1267 | </cells> | |||
|
|
1268 | </worksheet> | |||
|
|
1269 | </worksheets> | |||
|
|
1270 | </notebook> | |||
| @@ -1,56 +1,146 b'' | |||||
| 1 | #------------------------------------------------------------------------------- |
|
1 | # <nbformat>2</nbformat> | |
| 2 | # Driver code that the client runs. |
|
2 | ||
| 3 | #------------------------------------------------------------------------------- |
|
3 | # <markdowncell> | |
| 4 | # To run this code start a controller and engines using: |
|
4 | ||
| 5 | # ipcluster -n 2 |
|
5 | # # Eigenvalue distribution of Gaussian orthogonal random matrices | |
| 6 | # Then run the scripts by doing irunner rmt.ipy or by starting ipython and |
|
6 | ||
| 7 | # doing run rmt.ipy. |
|
7 | # <markdowncell> | |
| 8 |
|
8 | |||
| 9 | from rmtkernel import * |
|
9 | # The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices | |
| 10 | import numpy |
|
10 | # from the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest | |
|
|
11 | # neighbor eigenvalue distribution $\rho(s)$. | |||
|
|
12 | ||||
|
|
13 | # <codecell> | |||
|
|
14 | ||||
|
|
15 | from rmtkernel import ensemble_diffs, normalize_diffs, GOE | |||
|
|
16 | import numpy as np | |||
| 11 | from IPython.parallel import Client |
|
17 | from IPython.parallel import Client | |
| 12 |
|
18 | |||
|
|
19 | # <markdowncell> | |||
| 13 |
|
20 | |||
| 14 | def wignerDistribution(s): |
|
21 | # ## Wigner's nearest neighbor eigenvalue distribution | |
|
|
22 | ||||
|
|
23 | # <markdowncell> | |||
|
|
24 | ||||
|
|
25 | # The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution | |||
|
|
26 | # for the GOE: | |||
|
|
27 | # | |||
|
|
28 | # $$\rho(s) = \frac{\pi s}{2} \exp(-\pi s^2/4)$$ | |||
|
|
29 | ||||
|
|
30 | # <codecell> | |||
|
|
31 | ||||
|
|
32 | def wigner_dist(s): | |||
| 15 | """Returns (s, rho(s)) for the Wigner GOE distribution.""" |
|
33 | """Returns (s, rho(s)) for the Wigner GOE distribution.""" | |
| 16 |
return (n |
|
34 | return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.) | |
| 17 |
|
35 | |||
|
|
36 | # <codecell> | |||
| 18 |
|
37 | |||
| 19 |
def generate |
|
38 | def generate_wigner_data(): | |
| 20 |
s = n |
|
39 | s = np.linspace(0.0,4.0,400) | |
| 21 |
rhos = wigner |
|
40 | rhos = wigner_dist(s) | |
| 22 | return s, rhos |
|
41 | return s, rhos | |
| 23 |
|
||||
| 24 |
|
42 | |||
| 25 | def serialDiffs(num, N): |
|
43 | # <codecell> | |
| 26 | diffs = ensembleDiffs(num, N) |
|
44 | ||
| 27 | normalizedDiffs = normalizeDiffs(diffs) |
|
45 | s, rhos = generate_wigner_data() | |
| 28 | return normalizedDiffs |
|
46 | ||
|
|
47 | # <codecell> | |||
|
|
48 | ||||
|
|
49 | plot(s, rhos) | |||
|
|
50 | xlabel('Normalized level spacing s') | |||
|
|
51 | ylabel('Probability $\rho(s)$') | |||
|
|
52 | ||||
|
|
53 | # <markdowncell> | |||
|
|
54 | ||||
|
|
55 | # ## Serial calculation of nearest neighbor eigenvalue distribution | |||
|
|
56 | ||||
|
|
57 | # <markdowncell> | |||
|
|
58 | ||||
|
|
59 | # In this section we numerically construct and diagonalize a large number of GOE random matrices | |||
|
|
60 | # and compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core. | |||
|
|
61 | ||||
|
|
62 | # <codecell> | |||
|
|
63 | ||||
|
|
64 | def serial_diffs(num, N): | |||
|
|
65 | """Compute the nearest neighbor distribution for num NxX matrices.""" | |||
|
|
66 | diffs = ensemble_diffs(num, N) | |||
|
|
67 | normalized_diffs = normalize_diffs(diffs) | |||
|
|
68 | return normalized_diffs | |||
|
|
69 | ||||
|
|
70 | # <codecell> | |||
|
|
71 | ||||
|
|
72 | serial_nmats = 1000 | |||
|
|
73 | serial_matsize = 50 | |||
|
|
74 | ||||
|
|
75 | # <codecell> | |||
|
|
76 | ||||
|
|
77 | %timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize) | |||
| 29 |
|
78 | |||
|
|
79 | # <codecell> | |||
| 30 |
|
80 | |||
| 31 | def parallelDiffs(rc, num, N): |
|
81 | serial_diffs = serial_diffs(serial_nmats, serial_matsize) | |
|
|
82 | ||||
|
|
83 | # <markdowncell> | |||
|
|
84 | ||||
|
|
85 | # The numerical computation agrees with the predictions of Wigner, but it would be nice to get more | |||
|
|
86 | # statistics. For that we will do a parallel computation. | |||
|
|
87 | ||||
|
|
88 | # <codecell> | |||
|
|
89 | ||||
|
|
90 | hist_data = hist(serial_diffs, bins=30, normed=True) | |||
|
|
91 | plot(s, rhos) | |||
|
|
92 | xlabel('Normalized level spacing s') | |||
|
|
93 | ylabel('Probability $P(s)$') | |||
|
|
94 | ||||
|
|
95 | # <markdowncell> | |||
|
|
96 | ||||
|
|
97 | # ## Parallel calculation of nearest neighbor eigenvalue distribution | |||
|
|
98 | ||||
|
|
99 | # <markdowncell> | |||
|
|
100 | ||||
|
|
101 | # Here we perform a parallel computation, where each process constructs and diagonalizes a subset of | |||
|
|
102 | # the overall set of random matrices. | |||
|
|
103 | ||||
|
|
104 | # <codecell> | |||
|
|
105 | ||||
|
|
106 | def parallel_diffs(rc, num, N): | |||
| 32 | nengines = len(rc.targets) |
|
107 | nengines = len(rc.targets) | |
| 33 | num_per_engine = num/nengines |
|
108 | num_per_engine = num/nengines | |
| 34 | print "Running with", num_per_engine, "per engine." |
|
109 | print "Running with", num_per_engine, "per engine." | |
| 35 |
ar = rc.apply_async(ensemble |
|
110 | ar = rc.apply_async(ensemble_diffs, num_per_engine, N) | |
| 36 |
|
|
111 | diffs = np.array(ar.get()).flatten() | |
| 37 |
|
112 | normalized_diffs = normalize_diffs(diffs) | ||
| 38 |
|
113 | return normalized_diffs | ||
| 39 | # Main code |
|
114 | ||
| 40 | if __name__ == '__main__': |
|
115 | # <codecell> | |
| 41 | rc = Client() |
|
116 | ||
| 42 | view = rc[:] |
|
117 | client = Client() | |
| 43 | print "Distributing code to engines..." |
|
118 | view = client[:] | |
| 44 |
|
|
119 | view.run('rmtkernel.py') | |
| 45 |
|
|
120 | view.block = False | |
| 46 |
|
121 | |||
| 47 | # Simulation parameters |
|
122 | # <codecell> | |
| 48 | nmats = 100 |
|
123 | ||
| 49 | matsize = 30 |
|
124 | parallel_nmats = 40*serial_nmats | |
| 50 | # tic = time.time() |
|
125 | parallel_matsize = 50 | |
| 51 | %timeit -r1 -n1 serialDiffs(nmats,matsize) |
|
126 | ||
| 52 | %timeit -r1 -n1 parallelDiffs(view, nmats, matsize) |
|
127 | # <codecell> | |
| 53 |
|
128 | |||
| 54 | # Uncomment these to plot the histogram |
|
129 | %timeit -r1 -n1 parallel_diffs(view, parallel_nmats, parallel_matsize) | |
| 55 | # import pylab |
|
130 | ||
| 56 | # pylab.hist(parallelDiffs(rc,matsize,matsize)) |
|
131 | # <codecell> | |
|
|
132 | ||||
|
|
133 | pdiffs = parallel_diffs(view, parallel_nmats, parallel_matsize) | |||
|
|
134 | ||||
|
|
135 | # <markdowncell> | |||
|
|
136 | ||||
|
|
137 | # Again, the agreement with the Wigner distribution is excellent, but now we have better | |||
|
|
138 | # statistics. | |||
|
|
139 | ||||
|
|
140 | # <codecell> | |||
|
|
141 | ||||
|
|
142 | hist_data = hist(pdiffs, bins=30, normed=True) | |||
|
|
143 | plot(s, rhos) | |||
|
|
144 | xlabel('Normalized level spacing s') | |||
|
|
145 | ylabel('Probability $P(s)$') | |||
|
|
146 | ||||
| @@ -2,43 +2,41 b'' | |||||
| 2 | # Core routines for computing properties of symmetric random matrices. |
|
2 | # Core routines for computing properties of symmetric random matrices. | |
| 3 | #------------------------------------------------------------------------------- |
|
3 | #------------------------------------------------------------------------------- | |
| 4 |
|
4 | |||
| 5 | import numpy |
|
5 | import numpy as np | |
| 6 |
ra = n |
|
6 | ra = np.random | |
| 7 |
la = n |
|
7 | la = np.linalg | |
| 8 |
|
8 | |||
| 9 | def GOE(N): |
|
9 | def GOE(N): | |
| 10 | """Creates an NxN element of the Gaussian Orthogonal Ensemble""" |
|
10 | """Creates an NxN element of the Gaussian Orthogonal Ensemble""" | |
| 11 | m = ra.standard_normal((N,N)) |
|
11 | m = ra.standard_normal((N,N)) | |
| 12 | m += m.T |
|
12 | m += m.T | |
| 13 | return m |
|
13 | return m/2 | |
| 14 |
|
14 | |||
| 15 |
|
15 | |||
| 16 |
def center |
|
16 | def center_eigenvalue_diff(mat): | |
| 17 | """Compute the eigvals of mat and then find the center eigval difference.""" |
|
17 | """Compute the eigvals of mat and then find the center eigval difference.""" | |
| 18 | N = len(mat) |
|
18 | N = len(mat) | |
| 19 |
evals = n |
|
19 | evals = np.sort(la.eigvals(mat)) | |
| 20 | diff = evals[N/2] - evals[N/2-1] |
|
20 | diff = np.abs(evals[N/2] - evals[N/2-1]) | |
| 21 |
return diff |
|
21 | return diff | |
| 22 |
|
22 | |||
| 23 |
|
23 | |||
| 24 |
def ensemble |
|
24 | def ensemble_diffs(num, N): | |
| 25 |
"""Return |
|
25 | """Return num eigenvalue diffs for the NxN GOE ensemble.""" | |
| 26 | ensemble.""" |
|
26 | diffs = np.empty(num) | |
| 27 | diffs = numpy.empty(num) |
|
|||
| 28 | for i in xrange(num): |
|
27 | for i in xrange(num): | |
| 29 | mat = GOE(N) |
|
28 | mat = GOE(N) | |
| 30 |
diffs[i] = center |
|
29 | diffs[i] = center_eigenvalue_diff(mat) | |
| 31 | return diffs |
|
30 | return diffs | |
| 32 |
|
31 | |||
| 33 |
|
32 | |||
| 34 |
def normalize |
|
33 | def normalize_diffs(diffs): | |
| 35 | """Normalize an array of eigenvalue diffs.""" |
|
34 | """Normalize an array of eigenvalue diffs.""" | |
| 36 | return diffs/diffs.mean() |
|
35 | return diffs/diffs.mean() | |
| 37 |
|
36 | |||
| 38 |
|
37 | |||
| 39 |
def normalized |
|
38 | def normalized_ensemble_diffs(num, N): | |
| 40 |
"""Return |
|
39 | """Return num *normalized* eigenvalue diffs for the NxN GOE ensemble.""" | |
| 41 | GOE ensemble.""" |
|
40 | diffs = ensemble_diffs(num, N) | |
| 42 | diffs = ensembleDiffs(num, N) |
|
41 | return normalize_diffs(diffs) | |
| 43 | return normalizeDiffs(diffs) |
|
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| 44 |
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42 | |||
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