If I isolate the sim dw
cdp.mol[0].res[81].spin[0].dw_sim
Copy output from prompt to small test.py file.
---------------
import numpy as np
a = np.array([3.56199604639644, 3.7650948468782706, 3.7734884987727297,
3.642139129321543, 3.7666853635523303, 3.6315803417395633,
3.548618513663966, 3.5534026300790322, 3.6560050261342427,
3.559393613964833, 3.643742783984535, 3.8417557850398145,
3.633261341904902, 3.744204424801951, 3.6023836683438333,
3.629402707080922, 3.538289939828068, 3.853741712706393, 3.592047531696704,
3.6309378185683134, 3.768703169586926, 3.7862087881341924,
3.908928286097882, 3.693988276574685, 3.648907472307944, 3.534075058176779,
3.6270285971623712, 3.616447361371553, 3.518131195735667,
3.5921264889450617, 3.690282977512058, 3.5722828138859173,
3.6947690157713327, 3.353267973165118, 3.661855199312702,
3.7481230080917847, 3.8072741676273836, 3.5717863863755435,
3.82647524415927, 3.638932239485707, 3.8627381794143854,
3.8562874852691067, 3.8251946462546695, 3.718900815941118,
3.669183187562206, 3.6812860050150054, 3.596567337492485,
3.643255044215514, 3.8358609617626023, 3.589885966376562,
3.768422008946675, 3.6442067782560175, 3.5360631161686857,
3.706309147308013, 3.723441628305323, 3.7019914055614977,
3.673276772934729, 3.8785449202641784, 3.7101861781755368,
3.693584107288858, 3.7125564094760852, 3.6762850609584126,
3.8157117257138813, 3.7018158644513677, 3.530279936895104,
3.7009902789169105, 3.5736495381254194, 3.7367811504330666,
3.58979193413104, 3.9020629160458435, 3.8045967460431678,
3.751451216439939, 3.602605771168253, 3.8125873436655437,
3.7304066459719567, 3.545396121222916, 3.7349432336572153,
3.807433568014422, 3.6249325786241045, 3.639882263177061,
3.713199134762557, 3.6579265879166387, 3.6505364404873917,
3.7071566660282587, 3.691216327566044, 3.5966085864962483,
3.569229413366312, 3.5970618547062214, 3.6139477602545846,
3.742589813924136, 3.6080539317067366, 3.7445704966176265,
3.6052692253220435, 3.6939619008140188, 3.7352649601924823,
3.6882917261309167, 3.6430935861383418, 3.7128272377377862,
3.6392872820121713, 3.7835114014256046, 3.529329678378972,
3.666044605642382, 3.7284970021608967, 3.553633430947111,
3.768663233026153, 3.656077156065453, 3.6392136075179025,
3.5527517958434065, 3.627428688176744, 3.7004280072046627,
3.606779732841823, 3.5855768696462915, 3.817869184320143,
3.897885555994164, 3.4768562505555316, 3.678791336542599,
3.6809169528168866, 3.5212931732732358, 3.80645768572404,
3.5336477461052613, 3.6055908654129603, 3.689528484460423,
3.6505212536967067, 3.8323529554109808, 3.6858933288476177,
3.6759299817767026, 3.6610567688519535, 3.7860845954799114,
3.6603458115022933, 3.7785578086603673, 3.902354668299148,
3.8055861482832816, 3.682400719521998, 3.6940814556366712,
3.6252208208592855, 3.759148584942981, 3.586642522840439,
3.7634008748970116, 3.5863527479976023, 3.5813112066676016,
3.733249297200884, 3.680077657973306, 3.8146083646296454,
3.747622934450785, 3.751763729360891, 3.645216312057557, 3.627711031714324,
3.743806087120281, 3.754090422035011, 3.742774232386197,
3.7860885026891733, 3.644920826496917, 3.780760363373208,
3.740365533825603, 3.669239475114871, 3.749224549998295, 3.750666913036093,
3.5569128940937507, 3.6333489712976688, 3.572262098541855,
3.711118004907809, 3.452706649658693, 3.558697797848791,
3.6025802917377066, 3.51208420919845, 3.5302665430394917, 3.55047412533738,
3.620294642920974, 3.7125150288399498, 3.6550041859736337,
3.603145954616103, 3.7121712828648894, 3.7333345721954085,
3.9019178238038044, 3.5780488310855305, 3.8083092600730017,
3.797783381443727, 3.7111414020359574, 3.768926895445126,
3.6076613938570112, 3.7389296153778795, 3.726560063335822,
3.7349172940580706, 3.6785139220194587, 3.8411499268808766,
3.651622883456889, 3.669203921911223, 3.595015138849902, 3.756782584819988,
3.6087945157651538, 3.7396909764952793, 3.611396900111491,
3.59007961047317, 3.5640398983331147, 3.6920519200575312,
3.738990802773734, 3.774977105422998, 3.721857153491999, 3.592398274058418,
3.8000979011525677, 3.6825876372773974, 3.6140358720411974,
3.7201112308069213, 3.5927342724618354, 3.742712877741823,
3.6514875821852755, 3.705261957243619, 3.628437422517394, 3.68329564116722,
3.5175049987405984, 3.599320535174879, 3.6935915027553365,
3.5405676308746257, 3.6527910534778965, 3.7256960669886547,
3.5597772960028324, 3.588324588188865, 3.64573890695546, 3.641769060029505,
3.6847044674901315, 3.642884904153416, 3.672823881791987,
3.7555366743294627, 3.595044846478588, 3.787432842343791, 3.69793757478375,
3.5497887067466842, 3.5319876432216883, 3.7990178094267852,
3.7034904333566594, 3.7678442363268303, 3.695625818451896,
3.702246796878483, 3.627921590685772, 3.4555314995683046,
3.510475880439481, 3.834770417574009, 3.7386669597544904,
3.608707401422296, 3.928008814872595, 3.7172939243178775,
3.5347692344509367, 3.6377139413072124, 3.697181566482374,
3.8270903158045617, 3.655938124012822, 3.5783349730929688,
3.545163722671805, 3.763731595802845, 3.827136249693701,
3.5315040544347793, 3.6539082795612003, 3.5504828404335957,
3.5965211896170697, 3.7383453161929863, 3.8230338366087846,
3.627048598848577, 3.648390897416328, 3.68423507795786, 3.731209731198659,
3.4895035362430056, 3.6374449477462196, 3.738202718950397,
3.6189134160348053, 3.8859540817918736, 3.7872602557645987,
3.6839955689687365, 3.7413041452971134, 3.603901617876547,
3.8365540177947937, 3.7158842958686216, 3.2963981313194726,
3.736167469104169, 3.8656069914966777, 3.7262452412039986,
3.724978564948022, 3.6589168669167558, 3.664106608644678,
3.742056188005555, 3.8567680797002497, 3.765771181189931,
3.7689943287070435, 3.634087470863829, 3.7226136514636368,
3.6537406678469733, 3.550330017121598, 3.8165218137921038,
3.6455485509170336, 3.845130518854767, 3.623238757561012,
3.566609365296191, 3.788642967459456, 3.716156258672269, 3.6805685585326,
3.548048892417176, 3.684046186449083, 3.6115550466423936,
3.7476621123455836, 3.7594284275941945, 3.7452154212960886,
3.778755194749163, 3.887944617108257, 3.5735069740281737,
3.7509438004088986, 3.6802535209188845, 3.64948595331264,
3.794517301646539, 3.6217363401946727, 3.775471683046014,
3.5507685750741795, 3.6702260704152847, 3.695832344198455,
3.737069310317808, 3.7364639796791064, 3.8111885735012363,
3.6828799822043363, 3.5905504540568227, 3.824738062392524,
4.042676512808615, 3.6735073333314805, 3.502646786485088,
3.844176767051466, 3.6796980511374544, 3.588035503647361,
3.617966158671891, 3.791087239054204, 3.60013459071069, 3.4973042068514006,
3.759983441435442, 3.627425278628225, 3.7586423436404175,
3.6017727846032477, 3.6484472425828733, 3.600534336317735,
3.868801773923902, 3.6132963094926627, 3.6788670048991268,
3.614449637405996, 3.5368492393788005, 3.6019019213313417,
3.6467572048010997, 3.416116665643858, 3.6882713886175242,
3.9069557842080362, 3.6796414531331094, 3.8916174686851086,
3.596086092442806, 3.774932329507215, 3.574571664292506, 3.714399822595807,
3.6782258762606173, 3.6111452730827622, 3.679108576762216,
3.753721487868907, 3.5303618921775186, 3.8277749029174366,
3.8401522443527094, 3.766379971223831, 3.6861534360317485,
3.5550297044806385, 3.6739038502770414, 3.671772792707439,
3.7338578866741976, 3.754569968976897, 3.738291618674821,
3.7451140911700147, 3.956986996037874, 3.659533650702718,
3.7629087492327002, 3.538961830330332, 3.64945003264093,
3.6754379430144057, 3.6410214735939714, 3.6355416921955594,
3.650130084247056, 3.654990988273896, 3.624597550900315, 3.611857211065539,
3.5212740223065886, 3.715534039331449, 3.714450430519905,
3.7534853922315774, 3.668464964433396, 3.7264569324756964,
3.5004061054556486, 3.636242554227535, 3.6730204013722325,
3.800543800567683, 3.6693157796465687, 3.5991580734901527,
3.617360841067001, 3.6124119625936086, 4.034372797816842,
3.6794178244196103, 3.7755133531239684, 3.652086361675156,
3.6789417844111263, 3.576979494013252, 3.5884566275601917,
3.907720773532597, 3.7366983411875676, 3.7209263704682103,
3.727741858292903, 3.6735954623574854, 3.6064683273770872,
3.527981107053151, 3.709532384633967, 3.6210295410876387,
3.5222801002248936, 3.612588149908407, 3.9935196468741347,
3.558406630946854, 3.617764158289809, 3.7166827125111688,
3.6347100066437212, 3.5441968356273046, 3.5469330782728843,
3.83116335720549, 3.737347598957387, 3.67149813503603, 3.786308325632029,
3.7181623591892983, 3.7649155408666894, 3.57986506214882,
3.763642217619097, 3.8194956188629416, 3.633104627807313,
3.5049092404229816, 3.6440334567798214, 3.5740465357274385,
3.667632546652383, 3.7108995833399225, 3.5434861227460077,
3.6892551147519415, 3.5502672036502476, 3.9151043751681835,
3.606290594298331, 3.690565157572469, 3.6958138113963095,
3.7097713814615805, 3.6545230012906513, 3.947909314488077,
3.6827400672386577, 4.039658211616647, 3.6410235972840623,
3.8449033455834245, 3.6059377230869973, 3.842894876528751,
3.6889860900134384, 3.8657177989633693, 3.8714533897701013,
3.6411304550499013, 3.5798238114264054, 3.6362013746759967,
3.6907899160776374, 3.7807936495026704, 3.80260320682006,
3.6298733640230676, 3.9267717523095103, 3.6307358437695623,
3.8003450781285357, 3.7366534878146145, 3.7110043904097756,
3.509567432311692, 3.6665317876739127, 3.6447054012102953,
3.7787998619198087, 3.7855562737282007, 3.7721213099168542,
3.8644241755674007, 3.799337950076101, 3.660762073429478,
3.5143731438571075, 3.7976485754984743, 3.6487372553885122,
3.79096684551853, 3.6236793979487083, 3.6421440085675076,
3.789013355064313, 3.6557979787054444, 3.936184181631426,
3.952142168637194, 3.667227295171413, 3.7187027458281854,
3.722518742325237, 3.7196252949813475, 3.869233283689267,
3.7411264918088323, 3.6898233820686173, 3.5684404868901165,
3.7151372493402075, 3.6738810532379143, 3.8093362437246796,
3.769659702158876, 3.673148858969734, 3.597894459097, 3.6436104862818874,
3.684957784861707, 3.6154569886122454, 3.6397883716309427])
print len(a)
print np.std(a, ddof=1)
-------
500
0.105036572661
-----------
But the error in relax is:
cdp.mol[0].res[81].spin[0].dw_err
0.046827775338478594
Is there something I am missing here?
Best
Troels
Troels Emtekær Linnet
2015-01-14 13:05 GMT+01:00 Troels Emtekær Linnet <tlinnet@xxxxxxxxx>:
Hi Edward.
When I do an MC simulation of say 500, I get the same error value for dw.
Is this because it goes through all spins, then through all sim states,
and collect the difference, and then do an error calculation?
I would expect, that each dw would have its own error, based on the 500
fittings.
Would this be wrong?
Best
Troels
Troels Emtekær Linnet
Re: Error calculation of individual parameter "dw" from MC