516 lines
9.1 KiB
Python
516 lines
9.1 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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dim3_physically_contigous = np.array([1944100892,
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1929198745,
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2147407618,
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2361683504,
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2229290045,
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1936107919,
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1950196981,
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2316564611,
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2415777784,
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2251930639,
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1917048962,
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2122919883,
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2305445935,
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2216085132,
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2061970506,
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2077573288,
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2415427574])
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dmin_3_physically_contigous = sum(dim3_physically_contigous)
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dim3_regular = np.array([2001205195,
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2037350408,
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2077998800,
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2064361816,
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2370004326,
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2366505116,
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2433485997,
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2388982491,
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2622710065,
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2231849577,
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2213896383,
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1971144730,
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2279336623,
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2384236727,
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2159066740,
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1872922315,
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2046840068])
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dmin_3_regular = sum(dim3_regular)
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dim6_contigous = np.array([2472219435,
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3473628174,
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3146805964,
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2747609863,
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3624063096,
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2299770299,
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2731333935,
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2500798504,
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2279879638,
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3989040226,
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2595662999,
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3715651239,
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3293638078,
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2723808934,
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4183888040,
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2437497235,
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3262957061,
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3260647309,
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2145129129,
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3874909746,
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2471935689,
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3143457211,
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3450920659,
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2570835020,
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4197209649,
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3325832132,
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3041911087,
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3848617519,
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1973739208,
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3773139796,
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2737410930,
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2907858299,
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3813970823,
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2856501924,
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3505228599,
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2432178891,
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2854895470,
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3602152695,
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2576232334
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])
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dim_6_contigous = sum(dim6_contigous)
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dim6_regular = np.array([3512147188,
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2931698338,
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3130637973,
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2799013235,
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3256184323,
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2992132432,
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3041359022,
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3094813437,
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3028071885,
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3380696386,
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3018331410,
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2899603552,
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3264907899,
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3002822837,
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3045671887,
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3119204499,
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2953573342,
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2963606839,
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2855193865,
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3043260399,
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2972252469,
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2503069946,
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2958386657,
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3033810534,
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3007362327,
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3026500873,
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3159826130,
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2504328570,
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4058464445,
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3218889301,
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3597912212,
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4181723542,
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3749432601,
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3654586898,
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3557679884,
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3322970044,
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3630597266,
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3130958213,
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1019981557])
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dim_6_regular = sum(dim6_regular)
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dim40_contigous = np.array([5271750211,
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5947977060,
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6406575235,
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5511255729,
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6423429240,
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5094278723,
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6242776302,
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6297848739,
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4590445845,
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6764711612,
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4610372742,
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5903997396,
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5747140551,
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6300303767,
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6640645385,
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4818280514,
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7823467298,
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5828843492,
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7619923263,
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7891459023,
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7387222066,
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7941541977,
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5433577677,
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7362215737,
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7053988636,
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7387626023,
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6945895832,
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4915107460,
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6745811484,
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7802741433,
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6808767570,
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7717542941,
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5557441777,
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6274197189,
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6839290749,
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6583819269,
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8175619486,
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7374930361,
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7496545906,
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5787152431,
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6970388902,
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7150342762,
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6374515148,
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7086240550,
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6014911926,
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6805497356,
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6787839300,
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6029236409,
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7045329429,
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5463327288,
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7023215119,
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6305754943,
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6040079901,
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7642513816,
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5399217904,
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7615536076,
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6947227611,
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6886438575,
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6525913645,
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5704035737,
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6001096710,
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4595121294,
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3891698114,
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5754907462,
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5581736391,
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8438909738,
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7281420759,
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9120364283,
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8975142455,
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6194068545,
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7645451782,
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6244703959,
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8436588830,
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8530553210,
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7617848374,
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8172082546,
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6812387244,
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8703432529,
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8613319990,
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7501293173,
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8216594940,
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5673006642,
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7168602771,
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7846317702,
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7094119540,
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8854752613,
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5455608081,
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8407641178,
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8338729110,
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8119635911,
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8981010962,
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7495479592,
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8554574662,
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8404630428,
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7519304345,
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7136761544,
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6528504686,
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8236643504,
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6831632676,
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8347118009,
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7641263754,
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5707359038,
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6748898177,
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6066310601,
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6943861727,
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7098959658,
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5212148705,
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6824640874,
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5583585529,
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8003156647,
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6415970122,
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6830508723,
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7041516118,
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6892969782,
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7126446374,
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8238572592,
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6702708346,
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6612046729,
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6731145590,
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7182643495,
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6166401716,
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6391553234,
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6638759846,
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5539008636,
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8406967616,
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7660149216,
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8431551979,
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8269144799,
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7734313131,
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8403891519,
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7258535328,
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7776937659,
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6064178686,
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5770876015,
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6982161319,
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5235604055,
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8101527192,
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6046956898,
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6827456214,
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7874652143,
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5025716700,
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7506539290,
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6196558003,
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6656436732,
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5956327575,
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4626891040,
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6350370528,
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5249154115,
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7566366652,
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7035152421,
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5894510533,
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7071341874,
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6305872895,
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7188950553,
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6518432441,
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5956890819,
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7441553601,
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6285032471,
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6776691911,
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5916079446,
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2984166525
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])
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dim_40_contigous = sum(dim40_contigous)
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dim40_regular = np.array([int(x) for x in """7320333725
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8041544574
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6359081713
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7676718410
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7408541973
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8549261018
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6455940864
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7638044083
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7852961154
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8105713414
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8099296359
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8588846829
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9264198897
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7866569227
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8241210929
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8266772980
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9025896341
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8543734536
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8436388827
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8593700407
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7449440289
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8297109440
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8213614706
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9250000125
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7852953217
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8789498566
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7365356677
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7663656239
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7930037512
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7141236610
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7737423168
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8306296388
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8693384934
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7433924991
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7878827892
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7671595716
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7150009037
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8591898274
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8167375582
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8355489369
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7173398288
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8066437344
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6594406377
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7699310879
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7502720090
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7940562130
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6738407130
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7831934111
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7551421487
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8597892604
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7251882911
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7497411167
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7623185317
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6601524684
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8174240923
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7425424118
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7689177096
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5496078729
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5695648325
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7096458210
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4812243000
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8265423889
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7406646505
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8401794675
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8029187256
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8262191583
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8410556911
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8215142659
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7977382227
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8090414614
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8943953292
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7822463926
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7959987169
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7730265183
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8736349787
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8143049809
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6640249510
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7530281166
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6842877170
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7512789107
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7638804649
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7995198730
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7496218179
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7181518949
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8254413848
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8022381985
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7361988882
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7810130859
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7417215012
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7872443581
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7732389294
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7814055171
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7463971008
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8433179161
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7010361542
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8758722649
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7834208169
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7467025814
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7909486359
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7462899259
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8890074348
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8907995744
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6612293480
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7843416231
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8411079708
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9525606221
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6937922478
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8619142087
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7559808728
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7173215681
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7652102219
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8244303885
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7102114957
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6526600396
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7151648008
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7526924470
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8040860568
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8175611289
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7459798487
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7145058572
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7345908520
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8165157302
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7460488360
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8628914421
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8583302346
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7934022758
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7056421549
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8275846606
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8608406256
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7556324607
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7738815689
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7213420720
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7365304658
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7297146598""".replace(' ',',').replace('\n','').split(",")])
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dim_40_regular = sum(dim40_regular)
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# dimentions = ("3-dementions", "6-dementions", "40-dementions")
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# comparitors = {
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# 'FAT-Pointer based range address': (dmin_3_physically_contigous, dim_6_contigous, dim_40_contigous),
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# 'System Allocator': (dmin_3_regular, dim_6_regular, dim_40_regular),
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# }
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# x = np.arange(len(dimentions)) # the label locations
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# width = 0.25 # the width of the bars
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# multiplier = 0
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# fig, ax = plt.subplots(layout='constrained')
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# for attribute, measurement in comparitors.items():
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# offset = width * multiplier
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# rects = ax.bar(x + offset, measurement, width, label=attribute)
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# ax.bar_label(rects, padding=3)
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# multiplier += 1
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# # Add some text for labels, title and custom x-axis tick labels, etc.
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# ax.set_ylabel('DTLB L1 reads')
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# ax.set_title('L1D_TLB')
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# ax.set_xticks(x + width, dimentions)
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# ax.legend(loc='upper left', ncols=2)
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# ax.set_ylim(0, 250)
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# plt.show()
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# Sample data
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categories = ['3 dimentions', '6 dimentions', '40 dimentions']
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group_1 = [dmin_3_physically_contigous, dim_6_contigous, dim_40_contigous]
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group_2 = [dmin_3_regular, dim_6_regular, dim_40_regular]
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# Number of categories
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n = len(categories)
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# Create a bar width
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bar_width = 0.25
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# Create an array with the positions of the bars on the x-axis
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r1 = np.arange(n)
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r2 = [x + bar_width for x in r1]
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# r3 = [x + bar_width for x in r2]
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# Create the grouped bar graph
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plt.bar(r1, group_1, color='b', width=bar_width, edgecolor='grey', label='FAT-Pointer based range based addresses')
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plt.bar(r2, group_2, color='g', width=bar_width, edgecolor='grey', label='System memory allocator')
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# plt.bar(r3, group_3, color='r', width=bar_width, edgecolor='grey', label='Group 3')
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# Add xticks on the middle of the grouped bars
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plt.xlabel('Number of dimentions COZ kmeans', fontweight='bold')
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plt.xticks([r + bar_width for r in range(n)], categories)
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# Add labels and title
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plt.ylabel('DTLB L1 hits', fontweight='bold')
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plt.title('Sum of DTLB L1 hits')
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# Add a legend
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plt.legend()
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# Show the plot
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plt.show() |