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19 | 19 | "editable": true
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20 | 20 | },
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21 | 21 | "outputs": [
|
22 |
| - { |
23 |
| - "name": "stderr", |
24 |
| - "output_type": "stream", |
25 |
| - "text": [ |
26 |
| - "/usr/local/Cellar/python/3.6.4_3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", |
27 |
| - " return f(*args, **kwds)\n", |
28 |
| - "/usr/local/Cellar/python/3.6.4_3/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", |
29 |
| - " return f(*args, **kwds)\n" |
30 |
| - ] |
31 |
| - }, |
32 | 22 | {
|
33 | 23 | "name": "stdout",
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34 | 24 | "output_type": "stream",
|
|
77 | 67 | "Created evaluation tree with 1 subtrees. Will divide 1 procs into 1 (subtree-processing)\n",
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78 | 68 | " groups of ~1 procs each, to distribute over (1616,1616) params (taken as 1616,4 param groups of ~1,404 params).\n",
|
79 | 69 | " Memory estimate = 2.08GB (cache=1317, wrtLen1=1, wrtLen2=404, subsPerProc=1).\n",
|
80 |
| - "rank 0: 31.5849s: block 0/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
81 |
| - "rank 0: 63.3639s: block 1/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
82 |
| - "rank 0: 94.6396s: block 2/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
83 |
| - "rank 0: 125.672s: block 3/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
84 |
| - "rank 0: 156.941s: block 4/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
85 |
| - "rank 0: 188.128s: block 5/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
86 |
| - "rank 0: 219.359s: block 6/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
87 |
| - "rank 0: 250.692s: block 7/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
88 |
| - "rank 0: 282.103s: block 8/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
89 |
| - "rank 0: 313.297s: block 9/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
90 |
| - "rank 0: 344.539s: block 10/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
91 |
| - "rank 0: 375.918s: block 11/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
92 |
| - "rank 0: 407.192s: block 12/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
93 |
| - "rank 0: 438.699s: block 13/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
94 |
| - "rank 0: 469.997s: block 14/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
95 |
| - "rank 0: 501.103s: block 15/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
96 |
| - "rank 0: 532.714s: block 16/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
97 |
| - "rank 0: 563.916s: block 17/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
98 |
| - "rank 0: 595.099s: block 18/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
99 |
| - "rank 0: 626.301s: block 19/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
100 |
| - "rank 0: 657.771s: block 20/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
101 |
| - "rank 0: 689.13s: block 21/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
102 |
| - "rank 0: 720.577s: block 22/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
103 |
| - "rank 0: 752.159s: block 23/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
104 |
| - "rank 0: 783.494s: block 24/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
105 |
| - "rank 0: 814.859s: block 25/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
106 |
| - "rank 0: 846.3s: block 26/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
107 |
| - "rank 0: 877.769s: block 27/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
108 |
| - "rank 0: 909.556s: block 28/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
109 |
| - "rank 0: 940.868s: block 29/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
110 |
| - "rank 0: 972.242s: block 30/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
111 |
| - "rank 0: 1003.59s: block 31/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
112 |
| - "rank 0: 1034.86s: block 32/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
113 |
| - "rank 0: 1066.31s: block 33/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
114 |
| - "rank 0: 1097.8s: block 34/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
115 |
| - "rank 0: 1129.27s: block 35/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
116 |
| - "rank 0: 1160.66s: block 36/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
117 |
| - "rank 0: 1192.03s: block 37/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
118 |
| - "rank 0: 1223.11s: block 38/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
119 |
| - "rank 0: 1254.42s: block 39/4043, sub-tree 0/1, sub-tree-len = 1317\n" |
| 70 | + "rank 0: 62.0252s: block 0/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 71 | + "rank 0: 186.63s: block 1/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 72 | + "rank 0: 264.609s: block 2/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 73 | + "rank 0: 323.547s: block 3/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 74 | + "rank 0: 375.697s: block 4/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 75 | + "rank 0: 427.413s: block 5/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 76 | + "rank 0: 478.208s: block 6/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 77 | + "rank 0: 529.082s: block 7/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 78 | + "rank 0: 579.09s: block 8/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 79 | + "rank 0: 629.437s: block 9/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 80 | + "rank 0: 680.01s: block 10/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 81 | + "rank 0: 731.036s: block 11/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 82 | + "rank 0: 781.932s: block 12/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 83 | + "rank 0: 830.957s: block 13/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 84 | + "rank 0: 881.291s: block 14/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 85 | + "rank 0: 932.688s: block 15/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 86 | + "rank 0: 983.036s: block 16/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 87 | + "rank 0: 1034.89s: block 17/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 88 | + "rank 0: 1086.1s: block 18/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 89 | + "rank 0: 1138.17s: block 19/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 90 | + "rank 0: 1189.02s: block 20/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 91 | + "rank 0: 1251.38s: block 21/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 92 | + "rank 0: 1291.58s: block 22/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 93 | + "rank 0: 1323.17s: block 23/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 94 | + "rank 0: 1355.24s: block 24/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 95 | + "rank 0: 1387.11s: block 25/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 96 | + "rank 0: 1418.73s: block 26/4043, sub-tree 0/1, sub-tree-len = 1317\n", |
| 97 | + "rank 0: 1452.06s: block 27/4043, sub-tree 0/1, sub-tree-len = 1317\n" |
120 | 98 | ]
|
121 | 99 | },
|
122 | 100 | {
|
|
130 | 108 | "\u001b[0;32m/Volumes/Research/enielse_research/pyGSTi/packages/pygsti/objects/confidenceregionfactory.py\u001b[0m in \u001b[0;36mcompute_hessian\u001b[0;34m(self, comm, memLimit, approximate)\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0mminProbClip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprobClipInterval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemLimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemLimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m gateLabelAliases=aliases)\n\u001b[0m\u001b[1;32m 242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m nonMarkRadiusSq = max( 2*(_tools.logl_max(gateset, dataset)\n",
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131 | 109 | "\u001b[0;32m/Volumes/Research/enielse_research/pyGSTi/packages/pygsti/tools/likelihoodfns.py\u001b[0m in \u001b[0;36mlogl_hessian\u001b[0;34m(gateset, dataset, gatestring_list, minProbClip, probClipInterval, radius, poissonPicture, check, comm, memLimit, gateLabelAliases, verbosity)\u001b[0m\n\u001b[1;32m 696\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmySliceTupList\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 697\u001b[0m for (slice1,slice2,hprobs,dprobs12) in gateset.bulk_hprobs_by_block(\n\u001b[0;32m--> 698\u001b[0;31m evalSubTree, mySliceTupList, True, blkComm):\n\u001b[0m\u001b[1;32m 699\u001b[0m \u001b[0mrank\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGet_rank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcomm\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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132 | 110 | "\u001b[0;32m/Volumes/Research/enielse_research/pyGSTi/packages/pygsti/objects/gatematrixcalc.py\u001b[0m in \u001b[0;36mbulk_hprobs_by_block\u001b[0;34m(self, evalTree, wrtSlicesList, bReturnDProbs12, comm)\u001b[0m\n\u001b[1;32m 2752\u001b[0m hProdCache = self._compute_hproduct_cache(\n\u001b[1;32m 2753\u001b[0m \u001b[0mevalTree\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprodCache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdProdCache1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdProdCache2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2754\u001b[0;31m scaleCache, comm, wrtSlice1, wrtSlice2)\n\u001b[0m\u001b[1;32m 2755\u001b[0m \u001b[0mhGs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevalTree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinal_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhProdCache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[1;32m 2756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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133 |
| - "\u001b[0;32m/Volumes/Research/enielse_research/pyGSTi/packages/pygsti/objects/gatematrixcalc.py\u001b[0m in \u001b[0;36m_compute_hproduct_cache\u001b[0;34m(self, evalTree, prodCache, dProdCache1, dProdCache2, scaleCache, comm, wrtSlice1, wrtSlice2)\u001b[0m\n\u001b[1;32m 1185\u001b[0m \u001b[0;31m# Note: L, R = GxG ; dL,dR = vgs x GxG ; hL,hR = vgs x vgs x GxG\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1187\u001b[0;31m \u001b[0mdLdRa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdL1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdR2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1188\u001b[0m \u001b[0mdLdRb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdL2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdR1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1189\u001b[0m \u001b[0mdLdR_sym\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdLdRa\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdLdRb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
| 111 | + "\u001b[0;32m/Volumes/Research/enielse_research/pyGSTi/packages/pygsti/objects/gatematrixcalc.py\u001b[0m in \u001b[0;36m_compute_hproduct_cache\u001b[0;34m(self, evalTree, prodCache, dProdCache1, dProdCache2, scaleCache, comm, wrtSlice1, wrtSlice2)\u001b[0m\n\u001b[1;32m 1189\u001b[0m \u001b[0mdLdR_sym\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdLdRa\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswapaxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdLdRb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m \u001b[0mhProdCache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdLdR_sym\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_np\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhR\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[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\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 1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1193\u001b[0m \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaleCache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscaleCache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0miLeft\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mscaleCache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0miRight\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", |
134 | 112 | "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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135 | 113 | ]
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136 | 114 | }
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