I have a data that are being recorded and the time is used in epoch. I want to draw a figure where the x axis from zero to the time that the recorder stopped recording in seconds.
From my search I found that the output for decoding is a string that represent a date for example 01-Jan-1970 00:00:01 for all the data. how can I get different data using variable double or float array with the seconds only.
The method I used is :
output = datastr(datenum('1970','yyyy') + **timeinepoch** / 86400)
The problem is that all numbers give me the same output ?? and I want my data to be in array of seconds
here is a sample of my data timeinepoch
1.431344867477355
1.431344857732564
1.431344856832909
Here is the full data set:
1.43134485683291 1.43134485693360 1.43134485703513 1.43134485713490 1.43134485723280 1.43134485733824 1.43134485743525 1.43134485753449 1.43134485763714 1.43134485773256 1.43134485783254 1.43134485793235 1.43134485803481 1.43134485813486 1.43134485823259 1.43134485835510 1.43134485845240 1.43134485855250 1.43134485863259 1.43134485875249 1.43134485901518 1.43134485905522 1.43134485915475 1.43134485925273 1.43134485935277 1.43134485961262 1.43134485965299 1.43134485975277 1.43134486003268 1.43134486005289 1.43134486015272 1.43134486025242 1.43134486035263 1.43134486045254 1.43134486055257 1.43134486065708 1.43134486075720 1.43134486085483 1.43134486095482 1.43134486105451 1.43134486115239 1.43134486125257 1.43134486135232 1.43134486145473 1.43134486156251 1.43134486166082 1.43134486175443 1.43134486185417 1.43134486195630 1.43134486205711 1.43134486215445 1.43134486225478 1.43134486235478 1.43134486245782 1.43134486255472 1.43134486265428 1.43134486275417 1.43134486285418 1.43134486295735 1.43134486305517 1.43134486315859 1.43134486325497 1.43134486349503 1.43134486371503 1.43134486375591 1.43134486385563 1.43134486397315 1.43134486407340 1.43134486417604 1.43134486427350 1.43134486437951 1.43134486455631 1.43134486457633 1.43134486467658 1.43134486477340 1.43134486497348 1.43134486507374 1.43134486517375 1.43134486527400 1.43134486537473 1.43134486547398 1.43134486557386 1.43134486567653 1.43134486577353 1.43134486587349 1.43134486597379 1.43134486607428 1.43134486617383 1.43134486628100 1.43134486637383 1.43134486647394 1.43134486657705 1.43134486680117 1.43134486687743 1.43134486697392 1.43134486707380 1.43134486717390 1.43134486727392 1.43134486737474 1.43134486747735 1.43134486757405 1.43134486767487 1.43134486777720 1.43134486787406 1.43134486797408 1.43134486807391 1.43134486819397 1.43134486829631 1.43134486840328 1.43134486849407 1.43134486859779 1.43134486869804 1.43134486879936 1.43134486889866 1.43134486899881 1.43134486909768 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1.43134487552202 1.43134487561431 1.43134487571751 1.43134487581402 1.43134487591894 1.43134487602015 1.43134487613423 1.43134487621410 1.43134487631731 1.43134487641786 1.43134487659714 1.43134487673393 1.43134487681434 1.43134487701392 1.43134487703518 1.43134487713382 1.43134487723364 1.43134487733365 1.43134487743375 1.43134487759371 1.43134487773641 1.43134487785339 1.43134487793634 1.43134487803643 1.43134487813655 1.43134487823342 1.43134487849361 1.43134487851616 1.43134487853355 1.43134487863344 1.43134487873596 1.43134487883580 1.43134487893613 1.43134487919309 1.43134487923546 1.43134487933296 1.43134487943276 1.43134487953302 1.43134487963559 1.43134487973314 1.43134487983300 1.43134487993340 1.43134488003292 1.43134488013271 1.43134488023378 1.43134488033261 1.43134488043273 1.43134488053349 1.43134488063484 1.43134488073241 1.43134488083240 1.43134488093250 1.43134488103234 1.43134488113241 1.43134488123247 1.43134488133228 1.43134488145383 1.43134488153225 1.43134488163231 1.43134488173227 1.43134488185237 1.43134488195241 1.43134488205459 1.43134488229584 1.43134488235311 1.43134488245397 1.43134488255799 1.43134488265537 1.43134488275233 1.43134488285382 1.43134488295301 1.43134488305791 1.43134488315417 1.43134488325425 1.43134488335773 1.43134488345386 1.43134488355616 1.43134488365414 1.43134488375686 1.43134488385423 1.43134488395399 1.43134488405443 1.43134488415406 1.43134488425404 1.43134488435406 1.43134488445726 1.43134488455672 1.43134488479412 1.43134488511417 1.43134488513491 1.43134488515487 1.43134488549475 1.43134488551493 1.43134488575256 1.43134488577429 1.43134488585639 1.43134488595408 1.43134488607300 1.43134488617274 1.43134488627238 1.43134488637234 1.43134488647368 1.43134488657538 1.43134488667679 1.43134488677362 1.43134488687361 1.43134488697368 1.43134488707580 1.43134488717212 1.43134488727594 1.43134488737356 1.43134488747503 1.43134488757348 1.43134488767390 1.43134488777366 1.43134488787348 1.43134488797220 1.43134488807357 1.43134488817360 1.43134488827645 1.43134488837374 1.43134488847267 1.43134488857295 1.43134488867220 1.43134488877195 1.43134488887405 1.43134488897557 1.43134488907217 1.43134488917212 1.43134488927209 1.43134488937227 1.43134488947417 1.43134488957420 1.43134488967209 1.43134488977399 1.43134488987214 1.43134488997216 1.43134489007218 1.43134489017214 1.43134489027231 1.43134489037207 1.43134489049212 1.43134489057214 1.43134489069366 1.43134489077184 1.43134489087193 1.43134489099201 1.43134489109194 1.43134489119202 1.43134489129195 1.43134489139200 1.43134489149218 1.43134489159365 1.43134489169239 1.43134489179276 1.43134489189365 1.43134489199413 1.43134489209293 1.43134489219382 1.43134489229786 1.43134489239448 1.43134489249426 1.43134489259670 1.43134489269287 1.43134489279240 1.43134489289328 1.43134489299374 1.43134489309232 1.43134489319400 1.43134489329388 1.43134489339827 1.43134489349972 1.43134489360104 1.43134489369289 1.43134489379317 1.43134489389273 1.43134489399286 1.43134489409324 1.43134489419312 1.43134489429313 1.43134489439631 1.43134489449326 1.43134489459336 1.43134489469317 1.43134489479317 1.43134489489348 1.43134489501583 1.43134489509336 1.43134489519904 1.43134489531375 1.43134489541655 1.43134489551347 1.43134489561351 1.43134489571300 1.43134489581306 1.43134489591337 1.43134489601324 1.43134489611328 1.43134489621609 1.43134489631330 1.43134489641341 1.43134489651327 1.43134489661529 1.43134489671340 1.43134489681340 1.43134489691342 1.43134489701640 1.43134489711667 1.43134489722016 1.43134489731369 1.43134489741375 1.43134489751387 1.43134489761383 1.43134489771369 1.43134489781693 1.43134489791857 1.43134489801410 1.43134489823443 1.43134489831406 1.43134489841449 1.43134489851784 1.43134489862065 1.43134489871979 1.43134489882194 1.43134489892139 1.43134489902343 1.43134489911814 1.43134489921789 1.43134489931751 1.43134489941911 1.43134489952363 1.43134489961810 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1.43134491147783 1.43134491158736 1.43134491168111 1.43134491178022 1.43134491187874 1.43134491198209 1.43134491208459 1.43134491218077 1.43134491228186 1.43134491242085 1.43134491248290 1.43134491258096 1.43134491268100 1.43134491278144 1.43134491287854 1.43134491298102 1.43134491308044 1.43134491317801 1.43134491330101 1.43134491338228 1.43134491348084 1.43134491357880 1.43134491367504 1.43134491377597 1.43134491387936 1.43134491397945 1.43134491407589 1.43134491417503 1.43134491427849 1.43134491437575 1.43134491449874 1.43134491459858 1.43134491469459 1.43134491479845 1.43134491489875 1.43134491499527 1.43134491509476 1.43134491519451 1.43134491529460 1.43134491539452 1.43134491549823 1.43134491559851 1.43134491569782 1.43134491579436 1.43134491589408 1.43134491599410 1.43134491609538 1.43134491619407 1.43134491629527 1.43134491639455 1.43134491649542 1.43134491659813 1.43134491670053 1.43134491679437 1.43134491689410 1.43134491699513 1.43134491709744 1.43134491719402 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% vector of epoch times... (this is one of the values you posted)
epoch = 1.431344867477355;
% something is funky with your times, they are scaled
% by a factor of 1e9, so we'll fix that here
epoch = epoch * 1e9;
% epoch time -> MATLAB datenum
dnum = datenum(1970,1,1,0,0,epoch);
% split time into parts
[Y, M, D, H, MN, S] = datevec(dnum);
% S is the seconds
The output is:
Y =
2015
M =
5
D =
11
H =
11
MN =
47
S =
47.4774
This command gives you the milliseconds since epoch(1-Jan-1970) in matlab.
>> int64(milliseconds(datetime('now','Timezone','UTC')-datetime('1970-01-01','Timezone','UTC')))
I verified it using https://www.epochconverter.com/
Related
I found "unk" token in the glove vector file glove.6B.50d.txt downloaded from https://nlp.stanford.edu/projects/glove/. Its value is as follows:
unk -0.79149 0.86617 0.11998 0.00092287 0.2776 -0.49185 0.50195 0.00060792 -0.25845 0.17865 0.2535 0.76572 0.50664 0.4025 -0.0021388 -0.28397 -0.50324 0.30449 0.51779 0.01509 -0.35031 -1.1278 0.33253 -0.3525 0.041326 1.0863 0.03391 0.33564 0.49745 -0.070131 -1.2192 -0.48512 -0.038512 -0.13554 -0.1638 0.52321 -0.31318 -0.1655 0.11909 -0.15115 -0.15621 -0.62655 -0.62336 -0.4215 0.41873 -0.92472 1.1049 -0.29996 -0.0063003 0.3954
Is it a token to be used for unknown words or is it some kind of abbreviation?
The unk token in the pretrained GloVe files is not an unknown token!
See this google groups thread where Jeffrey Pennington (GloVe author) writes:
The pre-trained vectors do not have an unknown token, and currently the code just ignores out-of-vocabulary words when producing the co-occurrence counts.
It's an embedding learned like any other on occurrences of "unk" in the corpus (which appears to happen occasionally!)
Instead, Pennington suggests (in the same post):
...I've found that just taking an average of all or a subset of the word vectors produces a good unknown vector.
You can do that with the following code (should work with any pretrained GloVe file):
import numpy as np
GLOVE_FILE = 'glove.6B.50d.txt'
# Get number of vectors and hidden dim
with open(GLOVE_FILE, 'r') as f:
for i, line in enumerate(f):
pass
n_vec = i + 1
hidden_dim = len(line.split(' ')) - 1
vecs = np.zeros((n_vec, hidden_dim), dtype=np.float32)
with open(GLOVE_FILE, 'r') as f:
for i, line in enumerate(f):
vecs[i] = np.array([float(n) for n in line.split(' ')[1:]], dtype=np.float32)
average_vec = np.mean(vecs, axis=0)
print(average_vec)
For glove.6B.50d.txt this gives:
[-0.12920076 -0.28866628 -0.01224866 -0.05676644 -0.20210965 -0.08389011
0.33359843 0.16045167 0.03867431 0.17833012 0.04696583 -0.00285802
0.29099807 0.04613704 -0.20923874 -0.06613114 -0.06822549 0.07665912
0.3134014 0.17848536 -0.1225775 -0.09916984 -0.07495987 0.06413227
0.14441176 0.60894334 0.17463093 0.05335403 -0.01273871 0.03474107
-0.8123879 -0.04688699 0.20193407 0.2031118 -0.03935686 0.06967544
-0.01553638 -0.03405238 -0.06528071 0.12250231 0.13991883 -0.17446303
-0.08011883 0.0849521 -0.01041659 -0.13705009 0.20127155 0.10069408
0.00653003 0.01685157]
And because it is fairly compute intensive to do this with the larger glove files, I went ahead and computed the vector for glove.840B.300d.txt for you:
0.22418134 -0.28881392 0.13854356 0.00365387 -0.12870757 0.10243822 0.061626635 0.07318011 -0.061350107 -1.3477012 0.42037755 -0.063593924 -0.09683349 0.18086134 0.23704372 0.014126852 0.170096 -1.1491593 0.31497982 0.06622181 0.024687296 0.076693475 0.13851812 0.021302193 -0.06640582 -0.010336159 0.13523154 -0.042144544 -0.11938788 0.006948221 0.13333307 -0.18276379 0.052385733 0.008943111 -0.23957317 0.08500333 -0.006894406 0.0015864656 0.063391194 0.19177166 -0.13113557 -0.11295479 -0.14276934 0.03413971 -0.034278486 -0.051366422 0.18891625 -0.16673574 -0.057783455 0.036823478 0.08078679 0.022949161 0.033298038 0.011784158 0.05643189 -0.042776518 0.011959623 0.011552498 -0.0007971594 0.11300405 -0.031369694 -0.0061559738 -0.009043574 -0.415336 -0.18870236 0.13708843 0.005911723 -0.113035575 -0.030096142 -0.23908928 -0.05354085 -0.044904727 -0.20228513 0.0065645403 -0.09578946 -0.07391877 -0.06487607 0.111740574 -0.048649278 -0.16565254 -0.052037314 -0.078968436 0.13684988 0.0757494 -0.006275573 0.28693774 0.52017444 -0.0877165 -0.33010918 -0.1359622 0.114895485 -0.09744406 0.06269521 0.12118575 -0.08026362 0.35256687 -0.060017522 -0.04889904 -0.06828978 0.088740796 0.003964443 -0.0766291 0.1263925 0.07809314 -0.023164088 -0.5680669 -0.037892066 -0.1350967 -0.11351585 -0.111434504 -0.0905027 0.25174105 -0.14841858 0.034635577 -0.07334565 0.06320108 -0.038343467 -0.05413284 0.042197507 -0.090380974 -0.070528865 -0.009174437 0.009069661 0.1405178 0.02958134 -0.036431845 -0.08625681 0.042951006 0.08230793 0.0903314 -0.12279937 -0.013899368 0.048119213 0.08678239 -0.14450377 -0.04424887 0.018319942 0.015026873 -0.100526 0.06021201 0.74059093 -0.0016333034 -0.24960588 -0.023739101 0.016396184 0.11928964 0.13950661 -0.031624354 -0.01645025 0.14079992 -0.0002824564 -0.08052984 -0.0021310581 -0.025350995 0.086938225 0.14308536 0.17146006 -0.13943303 0.048792403 0.09274929 -0.053167373 0.031103406 0.012354865 0.21057427 0.32618305 0.18015954 -0.15881181 0.15322933 -0.22558987 -0.04200665 0.0084689725 0.038156632 0.15188617 0.13274793 0.113756925 -0.095273495 -0.049490947 -0.10265804 -0.27064866 -0.034567792 -0.018810693 -0.0010360252 0.10340131 0.13883452 0.21131058 -0.01981019 0.1833468 -0.10751636 -0.03128868 0.02518242 0.23232952 0.042052146 0.11731903 -0.15506615 0.0063580726 -0.15429358 0.1511722 0.12745973 0.2576985 -0.25486213 -0.0709463 0.17983761 0.054027 -0.09884228 -0.24595179 -0.093028545 -0.028203879 0.094398156 0.09233813 0.029291354 0.13110267 0.15682974 -0.016919162 0.23927948 -0.1343307 -0.22422817 0.14634751 -0.064993896 0.4703685 -0.027190214 0.06224946 -0.091360025 0.21490277 -0.19562101 -0.10032754 -0.09056772 -0.06203493 -0.18876675 -0.10963594 -0.27734384 0.12616494 -0.02217992 -0.16058226 -0.080475815 0.026953284 0.110732645 0.014894041 0.09416802 0.14299914 -0.1594008 -0.066080004 -0.007995227 -0.11668856 -0.13081996 -0.09237365 0.14741232 0.09180138 0.081735 0.3211204 -0.0036552632 -0.047030564 -0.02311798 0.048961394 0.08669574 -0.06766279 -0.50028914 -0.048515294 0.14144728 -0.032994404 -0.11954345 -0.14929578 -0.2388355 -0.019883996 -0.15917352 -0.052084364 0.2801028 -0.0029121689 -0.054581646 -0.47385484 0.17112483 -0.12066923 -0.042173345 0.1395337 0.26115036 0.012869649 0.009291686 -0.0026459037 -0.075331464 0.017840583 -0.26869613 -0.21820338 -0.17084768 -0.1022808 -0.055290595 0.13513643 0.12362477 -0.10980586 0.13980341 -0.20233242 0.08813751 0.3849736 -0.10653763 -0.06199595 0.028849555 0.03230154 0.023856193 0.069950655 0.19310954 -0.077677034 -0.144811
Since I can't comment, writing another answer.
If anyone's having trouble using the above vector given by #jayelm because copy pasting won't work. I am writing 2 lines of code that will give you the vector ready to be used in python.
vec_string = '0.22418134 -0.28881392 0.13854356 0.00365387 -0.12870757 0.10243822 0.061626635 0.07318011 -0.061350107 -1.3477012 0.42037755 -0.063593924 -0.09683349 0.18086134 0.23704372 0.014126852 0.170096 -1.1491593 0.31497982 0.06622181 0.024687296 0.076693475 0.13851812 0.021302193 -0.06640582 -0.010336159 0.13523154 -0.042144544 -0.11938788 0.006948221 0.13333307 -0.18276379 0.052385733 0.008943111 -0.23957317 0.08500333 -0.006894406 0.0015864656 0.063391194 0.19177166 -0.13113557 -0.11295479 -0.14276934 0.03413971 -0.034278486 -0.051366422 0.18891625 -0.16673574 -0.057783455 0.036823478 0.08078679 0.022949161 0.033298038 0.011784158 0.05643189 -0.042776518 0.011959623 0.011552498 -0.0007971594 0.11300405 -0.031369694 -0.0061559738 -0.009043574 -0.415336 -0.18870236 0.13708843 0.005911723 -0.113035575 -0.030096142 -0.23908928 -0.05354085 -0.044904727 -0.20228513 0.0065645403 -0.09578946 -0.07391877 -0.06487607 0.111740574 -0.048649278 -0.16565254 -0.052037314 -0.078968436 0.13684988 0.0757494 -0.006275573 0.28693774 0.52017444 -0.0877165 -0.33010918 -0.1359622 0.114895485 -0.09744406 0.06269521 0.12118575 -0.08026362 0.35256687 -0.060017522 -0.04889904 -0.06828978 0.088740796 0.003964443 -0.0766291 0.1263925 0.07809314 -0.023164088 -0.5680669 -0.037892066 -0.1350967 -0.11351585 -0.111434504 -0.0905027 0.25174105 -0.14841858 0.034635577 -0.07334565 0.06320108 -0.038343467 -0.05413284 0.042197507 -0.090380974 -0.070528865 -0.009174437 0.009069661 0.1405178 0.02958134 -0.036431845 -0.08625681 0.042951006 0.08230793 0.0903314 -0.12279937 -0.013899368 0.048119213 0.08678239 -0.14450377 -0.04424887 0.018319942 0.015026873 -0.100526 0.06021201 0.74059093 -0.0016333034 -0.24960588 -0.023739101 0.016396184 0.11928964 0.13950661 -0.031624354 -0.01645025 0.14079992 -0.0002824564 -0.08052984 -0.0021310581 -0.025350995 0.086938225 0.14308536 0.17146006 -0.13943303 0.048792403 0.09274929 -0.053167373 0.031103406 0.012354865 0.21057427 0.32618305 0.18015954 -0.15881181 0.15322933 -0.22558987 -0.04200665 0.0084689725 0.038156632 0.15188617 0.13274793 0.113756925 -0.095273495 -0.049490947 -0.10265804 -0.27064866 -0.034567792 -0.018810693 -0.0010360252 0.10340131 0.13883452 0.21131058 -0.01981019 0.1833468 -0.10751636 -0.03128868 0.02518242 0.23232952 0.042052146 0.11731903 -0.15506615 0.0063580726 -0.15429358 0.1511722 0.12745973 0.2576985 -0.25486213 -0.0709463 0.17983761 0.054027 -0.09884228 -0.24595179 -0.093028545 -0.028203879 0.094398156 0.09233813 0.029291354 0.13110267 0.15682974 -0.016919162 0.23927948 -0.1343307 -0.22422817 0.14634751 -0.064993896 0.4703685 -0.027190214 0.06224946 -0.091360025 0.21490277 -0.19562101 -0.10032754 -0.09056772 -0.06203493 -0.18876675 -0.10963594 -0.27734384 0.12616494 -0.02217992 -0.16058226 -0.080475815 0.026953284 0.110732645 0.014894041 0.09416802 0.14299914 -0.1594008 -0.066080004 -0.007995227 -0.11668856 -0.13081996 -0.09237365 0.14741232 0.09180138 0.081735 0.3211204 -0.0036552632 -0.047030564 -0.02311798 0.048961394 0.08669574 -0.06766279 -0.50028914 -0.048515294 0.14144728 -0.032994404 -0.11954345 -0.14929578 -0.2388355 -0.019883996 -0.15917352 -0.052084364 0.2801028 -0.0029121689 -0.054581646 -0.47385484 0.17112483 -0.12066923 -0.042173345 0.1395337 0.26115036 0.012869649 0.009291686 -0.0026459037 -0.075331464 0.017840583 -0.26869613 -0.21820338 -0.17084768 -0.1022808 -0.055290595 0.13513643 0.12362477 -0.10980586 0.13980341 -0.20233242 0.08813751 0.3849736 -0.10653763 -0.06199595 0.028849555 0.03230154 0.023856193 0.069950655 0.19310954 -0.077677034 -0.144811'
import numpy as np
average_glove_vector = np.array(vec_string.split(" "))
print(average_glove_vector)
In matlab, say I have a vector:
a = [0.001818443 0.002518386 0.003220336 0.005121008 0.005896566 0.006067186 0.006398329 0.006398407 0.008139209 0.008179847 0.009156658 0.010554859 0.010897468 0.011446512 0.011646556 0.013025077 0.013744086 0.014617856 0.014830682 0.014941674 0.015810817 0.015953685 0.016022438 0.01662184 0.017497373 0.018051816 0.018084908 0.019634522 0.02055418 0.021050675 0.021573997 0.022819828 0.024124407 0.02499767 0.025745151 0.026366919 0.029380223 0.029534435 0.029988118 0.033517171 0.037612906 0.038975473 0.045889922 0.04598939 0.045995714 0.046398796 0.047502087 0.048214192 0.050098857 0.053184072 0.055892719 0.057670042 0.059855042 0.061763503 0.065288778 0.066057267 0.066842804 0.067799739 0.069549949 0.070151584 0.071572852 0.084351815 0.084676596 0.088092878 0.088329724 0.089958848 0.09000399 0.092272165 0.092561834 0.092888091 0.093193395 0.095380669 0.100645564 0.101506295 0.104656067 0.105057051 0.10527394 0.107024689 0.107608649 0.108699789 0.109433298 0.111805635 0.111990726 0.112119163 0.113570537 0.115032624 0.123301615 0.125012672 0.127758465 0.131636854 0.132397883 0.132910662 0.136185494 0.137039002 0.138037293 0.139764132 0.140727482 0.141044291 0.141523184 0.142202565 0.142563209 0.145192227 0.146881752 0.149407941 0.152043489 0.153144103 0.153181689 0.153795372 0.15548845 0.158134807 0.159905274 0.160042269 0.160098768 0.160483614 0.164710118 0.166850893 0.167909098 0.167986172 0.169019205 0.173698541 0.174264483 0.17696023 0.177973442 0.182348008 0.192484859 0.193880575 0.197216088 0.197939827 0.198109045 0.199497862 0.200821988 0.203783239 0.20515055 0.206573438 0.207185798 0.209268239 0.209386896 0.210951877 0.212023024 0.213363487 0.214391326 0.221532866 0.223109522 0.225343507 0.22641709 0.228343987 0.229206318 0.231114414 0.234752015 0.237026193 0.237716285 0.240721044 0.242380638 0.245407553 0.246434245 0.247559843 0.247849238 0.253027915 0.256495379 0.257901113 0.258536851 0.259306669 0.259722973 0.269672307 0.269866791 0.277255751 0.282296038 0.283236904 0.286946602 0.290243971 0.291043929 0.29129401 0.295039384 0.306007647 0.318012708 0.31816788 0.323432941 0.327635294 0.331093206 0.332249017 0.336175038 0.338095934 0.34029032 0.34317957 0.343788501 0.344121892 0.354931564 0.35667793 0.363077884 0.366887155 0.367370204 0.368269666 0.368423655 0.372253797 0.373533628 0.376022623 0.380837432 0.38261496 0.38391646 0.387548982 0.390389207 0.391454753 0.392089937 0.393426643 0.399546308 0.400245613 0.410202767 0.410776868 0.411723243 0.413456921 0.415884043 0.417789874 0.420313779 0.421429254 0.422511926 0.425439714 0.432455519 0.436032696 0.437367296 0.440152475 0.451378258 0.451755234 0.452195559 0.461256878 0.462486697 0.466794805 0.468274083 0.470176312 0.471656017 0.476690087 0.476869314 0.479601939 0.480684241 0.48433634 0.48590973 0.487749692 0.488207469 0.492988875 0.494796745 0.498156249 0.4985104 0.504612947 0.504967424 0.50679421 0.507507695 0.508506011 0.509330529 0.510055669 0.513319728 0.52891378 0.534509478 0.539047817 0.53915951 0.541296687 0.545072863 0.552167571 0.554581857 0.561492014 0.563884635 0.56426438 0.565241463 0.568908009 0.571068798 0.571510656 0.574766859 0.574855866 0.578654534 0.580464471 0.580887919 0.586141679 0.586966123 0.588709274 0.589037758 0.590368048 0.591671348 0.595978448 0.596819829 0.600253407 0.606098247 0.606571956 0.607739275 0.619011439 0.621019538 0.621584422 0.621911881 0.625158613 0.626027273 0.630605849 0.648745354 0.651696835 0.652124313 0.653617078 0.658464598 0.658753146 0.659914726 0.660454081 0.663254931 0.666213877 0.66710949 0.671102828 0.672232077 0.677146974 0.679136682 0.684108254 0.685413256 0.695469558 0.699891217 0.70086933 0.702538999 0.703841471 0.706858722 0.708526759 0.719914701 0.720515776 0.722374479 0.729898476 0.73207238 0.735491621 0.747979179 0.748009317 0.756113996 0.765310468 0.767536693 0.773459493 0.776692483 0.78337608 0.785347933 0.793150735 0.802590758 0.803742726 0.803939618 0.804124158 0.820932029 0.822075589 0.822879052 0.823691124 0.828333937 0.828903924 0.829498182 0.831744237 0.839063743 0.841337078 0.84171847 0.859784354 0.866309271 0.866320299 0.866971295 0.867950201 0.871969488 0.874637485 0.880743292 0.88827141 0.893950376 0.895879931 0.896267286 0.896848582 0.898141369 0.901481362 0.910473103 0.911477969 0.913705886 0.926522732 0.930260635 0.930701078 0.93463515 0.934808147 0.935922692 0.936764529 0.93959948 0.939793801 0.944385802 0.944693766 0.947020268 0.947097561 0.950125145 0.952711274 0.95419146 0.956369148 0.9570486 0.958475028 0.969842914 0.981551219 0.989160935 0.992674135 0.995621611 1.004946727 1.012283438 1.015692666 1.024106096 1.030556733 1.032833092 1.03935138 1.03983552 1.047370018 1.063408518 1.063839459 1.065426157 1.068160918 1.069540521 1.077658708 1.087582646 1.087760841 1.094524538 1.098800356 1.1001774 1.107885842 1.108951101 1.111112066 1.113553249 1.118698485 1.118853872 1.124506817 1.125842913 1.140696534 1.145563833 1.163177388 1.172282341 1.172495225 1.175475612 1.176864362 1.17990075 1.182195585 1.189691417 1.19404695 1.201722268 1.207506808 1.207712757 1.217276704 1.226244517 1.231233705 1.235338344 1.235488468 1.237856505 1.246471414 1.248198622 1.24847215 1.249213122 1.253941679 1.255492087 1.261797214 1.272565441 1.275217172 1.279047194 1.2941275 1.296036211 1.303184537 1.303538428 1.304022026 1.306154543 1.313038716 1.325574114 1.325811852 1.329512083 1.335354282 1.336243495 1.349582224 1.349879286 1.349900576 1.351984631 1.352752927 1.380875102 1.381652802 1.385006548 1.385551407 1.399529901 1.400418216 1.400665522 1.402213831 1.407079557 1.407902968 1.426432367 1.427988618 1.430494495 1.436726215 1.455773986 1.457812821 1.457839075 1.464415454 1.465676132 1.471725239 1.478228056 1.493616649 1.505911081 1.510317752 1.517723479 1.519844042 1.520079555 1.52266623 1.528263582 1.528956908 1.539259874 1.54540469 1.552334282 1.559284266 1.560271081 1.560397723 1.58015835 1.584258957 1.586824283 1.5901615 1.601497093 1.616542626 1.623922568 1.655459388 1.656518418 1.658450231 1.668041767 1.677051536 1.683863037 1.685259883 1.694649161 1.698606755 1.703678803 1.713706728 1.717011705 1.723855901 1.724709626 1.730421966 1.74844195 1.749035987 1.756795943 1.763444653 1.775098962 1.779555761 1.818373597 1.827387112 1.843804423 1.84962867 1.86445448 1.867109055 1.88714409 1.889979731 1.891826361 1.892089882 1.899382375 1.902349674 1.905658074 1.914800201 1.922334951 1.927667035 1.948721828 1.954657495 1.965798927 1.966959101 1.979507708 1.980399089 2.002907363 2.005971219 2.017485761 2.025826577 2.031746032 2.038502827 2.044743806 2.054344832 2.069686195 2.097264843 2.099601236 2.110771315 2.112519282 2.119955879 2.121704608 2.127885945 2.132366059 2.138348477 2.14114747 2.141311108 2.163556408 2.164123085 2.172858142 2.199500097 2.205509788 2.219358975 2.238957728 2.249818992 2.2594448 2.269944498 2.2706867 2.280357357 2.282960593 2.295150671 2.318687227 2.321618718 2.334867032 2.335012279 2.344689038 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1920.950517 1926.326659 1928.176102 1929.506964 1934.604217 1939.777123 1939.9775 1941.755309 1943.528118 1944.940437 1948.146898 1949.393129 1952.669934 1957.382105 1974.814187 1974.930052 1977.787028 1978.05484 1981.278578 1985.544084 1985.951329 1987.672333 1989.859323 1999.003701 2000.29839 2010.240822 2011.18677 2013.788559 2015.608553 2017.241148 2018.726592 2023.237331 2024.367796 2025.012118 2030.088672 2030.858289 2031.91155 2047.429465 2056.103201 2060.681174 2067.853485 2068.090404 2070.455076 2074.196306 2075.368651 2079.204847 2085.470085 2085.729643 2088.787981 2090.90702 2096.795218 2098.107784 2109.1462 2111.881475 2112.3375 2119.125894 2132.544339 2134.309156 2137.225421 2137.34022 2140.171403 2141.529288 2150.369762 2151.661726 2154.312051 2155.000962 2156.770678 2157.759589 2164.041693 2164.805817 2165.059708 2168.65522 2169.206429 2174.040541 2188.917693 2192.494984 2208.212818 2208.768799 2216.977084 2218.692144 2220.102318 2230.953269 2234.154411 2234.405244 2235.065111 2236.344979 2249.802239 2264.686865 2267.396929 2273.404114 2273.754356 2277.413755 2280.6782 2283.149105 2285.348922 2289.328316 2290.734348 2295.517396 2312.503918 2320.990391 2322.061912 2323.440401 2324.878674 2333.165823 2333.252925 2335.603857 2337.325547 2345.946966 2358.273812 2366.02324 2370.471418 2371.784879 2375.909149 2379.075768 2385.552289 2389.641257 2389.86403 2390.725414 2395.819447 2407.106186 2408.175752 2429.088981 2434.82175 2440.124058 2467.929505 2471.566345 2477.609812 2489.945091 2491.580836 2510.302527 2511.638942 2515.044217 2515.71574 2517.028608 2524.354087 2524.877922 2526.06623 2535.558951 2535.721585 2536.492167 2537.325694 2537.709872 2539.365845 2540.281295 2553.669496 2558.765222 2571.722037 2572.527088 2574.079989 2582.056893 2582.866113 2588.746023 2591.215869 2599.739431 2608.250835 2609.567659 2609.624241 2610.649666 2619.595179 2621.563198 2636.421069 2639.394226 2642.023428 2669.445731 2675.382826 2680.034969 2694.084625 2696.268191 2704.531319 2707.839333 2716.792796 2730.732111 2732.069684 2737.677226 2778.443114 2782.215594 2785.874988 2791.253987 2791.436181 2801.164946 2805.848603 2806.178239 2810.666466 2813.901345 2830.859735 2853.498852 2856.717434 2868.888741 2870.367911 2878.874877 2883.703936 2885.632474 2905.874322 2907.515485 2916.389413 2923.073962 2929.712875 2931.019447 2933.791531 2933.930571 2935.077087 2949.087886 2976.158269 2981.768596 2983.915934 2989.715087 3001.266498 3002.479299 3011.896758 3011.912842 3020.089709 3025.493187 3042.22217 3043.797986 3044.370623 3045.683447 3053.071336 3054.37784 3059.479072 3063.92801 3107.161718 3151.462292 3173.531554 3256.077437 3256.892854 3259.605488 3265.533651 3289.866956 3319.895402 3333.730546 3378.930836 3388.87019 3396.586658 3408.947451 3436.547313 3439.845643 3464.238962 3465.187872 3465.352967 3498.880213 3522.537563 3533.001912 3553.327338 3600.815901 3603.355282 3717.619142 3755.870625 3760.009184 3773.018383 3783.863253 3811.612746 3811.771546 3814.024695 3867.8375 3883.205185 3886.970119 3908.597606 3911.469436 3917.313319 3939.971862 3986.038094 3987.670344 4046.750127 4066.433675 4092.504341 4097.021961 4169.546188 4186.34036 4187.66536 4190.45858 4204.426559 4212.053484 4243.227528 4250.384263 4254.988812 4260.547742 4262.13353 4320.226864 4367.875314 4388.094236 4536.069442 4564.327703 4567.306728 4604.70907 4646.504789 4671.757915 4699.991441 4787.618259 4876.00814 4904.917446 4917.17524 4925.327908 5011.724579 5098.552384 5303.654354 5308.235953 5309.20718 5392.941707 5419.906687 5475.854044 5495.805466 5568.353474 5640.966362 5729.170599 5756.300338 5806.041122 5832.62891 5835.703258 5915.919707 5927.430005 5981.107598 6030.061892 6096.21919 6167.676768 6201.442974 6225.328106 6260.287897 6289.796644 6322.660099 6326.143981 6469.370471 6476.395452 6499.135987 6644.829529 7103.246168 7336.643496 7450.887574 7463.63355 7724.153705 8125.302957 8315.141568 8480.871611 8667.155784 8993.192971 10269.69361 11689.13634 12457.90885 12725.48318 12877.43593];
I want to split a into ~10 discrete groups based on value to make a grouped boxplot of:
data = rand(2348,1);
so that:
boxplot(data,group)
The discrete groups could be like the x-axis in this plot:
Let's say we split into 10 groups by ascending order of values. Try this:
%Note I added 2 extra zero values to start of this matrix to make it divisible by 10
a = [0.000 0.000 0.001818443 0.002518386 0.003220336 0.005121008 0.005896566 0.006067186 0.006398329 0.006398407 0.008139209 0.008179847 0.009156658 0.010554859 0.010897468 0.011446512 0.011646556 0.013025077 0.013744086 0.014617856 0.014830682 0.014941674 0.015810817 0.015953685 0.016022438 0.01662184 0.017497373 0.018051816 0.018084908 0.019634522 0.02055418 0.021050675 0.021573997 0.022819828 0.024124407 0.02499767 0.025745151 0.026366919 0.029380223 0.029534435 0.029988118 0.033517171 0.037612906 0.038975473 0.045889922 0.04598939 0.045995714 0.046398796 0.047502087 0.048214192 0.050098857 0.053184072 0.055892719 0.057670042 0.059855042 0.061763503 0.065288778 0.066057267 0.066842804 0.067799739 0.069549949 0.070151584 0.071572852 0.084351815 0.084676596 0.088092878 0.088329724 0.089958848 0.09000399 0.092272165 0.092561834 0.092888091 0.093193395 0.095380669 0.100645564 0.101506295 0.104656067 0.105057051 0.10527394 0.107024689 0.107608649 0.108699789 0.109433298 0.111805635 0.111990726 0.112119163 0.113570537 0.115032624 0.123301615 0.125012672 0.127758465 0.131636854 0.132397883 0.132910662 0.136185494 0.137039002 0.138037293 0.139764132 0.140727482 0.141044291 0.141523184 0.142202565 0.142563209 0.145192227 0.146881752 0.149407941 0.152043489 0.153144103 0.153181689 0.153795372 0.15548845 0.158134807 0.159905274 0.160042269 0.160098768 0.160483614 0.164710118 0.166850893 0.167909098 0.167986172 0.169019205 0.173698541 0.174264483 0.17696023 0.177973442 0.182348008 0.192484859 0.193880575 0.197216088 0.197939827 0.198109045 0.199497862 0.200821988 0.203783239 0.20515055 0.206573438 0.207185798 0.209268239 0.209386896 0.210951877 0.212023024 0.213363487 0.214391326 0.221532866 0.223109522 0.225343507 0.22641709 0.228343987 0.229206318 0.231114414 0.234752015 0.237026193 0.237716285 0.240721044 0.242380638 0.245407553 0.246434245 0.247559843 0.247849238 0.253027915 0.256495379 0.257901113 0.258536851 0.259306669 0.259722973 0.269672307 0.269866791 0.277255751 0.282296038 0.283236904 0.286946602 0.290243971 0.291043929 0.29129401 0.295039384 0.306007647 0.318012708 0.31816788 0.323432941 0.327635294 0.331093206 0.332249017 0.336175038 0.338095934 0.34029032 0.34317957 0.343788501 0.344121892 0.354931564 0.35667793 0.363077884 0.366887155 0.367370204 0.368269666 0.368423655 0.372253797 0.373533628 0.376022623 0.380837432 0.38261496 0.38391646 0.387548982 0.390389207 0.391454753 0.392089937 0.393426643 0.399546308 0.400245613 0.410202767 0.410776868 0.411723243 0.413456921 0.415884043 0.417789874 0.420313779 0.421429254 0.422511926 0.425439714 0.432455519 0.436032696 0.437367296 0.440152475 0.451378258 0.451755234 0.452195559 0.461256878 0.462486697 0.466794805 0.468274083 0.470176312 0.471656017 0.476690087 0.476869314 0.479601939 0.480684241 0.48433634 0.48590973 0.487749692 0.488207469 0.492988875 0.494796745 0.498156249 0.4985104 0.504612947 0.504967424 0.50679421 0.507507695 0.508506011 0.509330529 0.510055669 0.513319728 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1.616542626 1.623922568 1.655459388 1.656518418 1.658450231 1.668041767 1.677051536 1.683863037 1.685259883 1.694649161 1.698606755 1.703678803 1.713706728 1.717011705 1.723855901 1.724709626 1.730421966 1.74844195 1.749035987 1.756795943 1.763444653 1.775098962 1.779555761 1.818373597 1.827387112 1.843804423 1.84962867 1.86445448 1.867109055 1.88714409 1.889979731 1.891826361 1.892089882 1.899382375 1.902349674 1.905658074 1.914800201 1.922334951 1.927667035 1.948721828 1.954657495 1.965798927 1.966959101 1.979507708 1.980399089 2.002907363 2.005971219 2.017485761 2.025826577 2.031746032 2.038502827 2.044743806 2.054344832 2.069686195 2.097264843 2.099601236 2.110771315 2.112519282 2.119955879 2.121704608 2.127885945 2.132366059 2.138348477 2.14114747 2.141311108 2.163556408 2.164123085 2.172858142 2.199500097 2.205509788 2.219358975 2.238957728 2.249818992 2.2594448 2.269944498 2.2706867 2.280357357 2.282960593 2.295150671 2.318687227 2.321618718 2.334867032 2.335012279 2.344689038 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2235.065111 2236.344979 2249.802239 2264.686865 2267.396929 2273.404114 2273.754356 2277.413755 2280.6782 2283.149105 2285.348922 2289.328316 2290.734348 2295.517396 2312.503918 2320.990391 2322.061912 2323.440401 2324.878674 2333.165823 2333.252925 2335.603857 2337.325547 2345.946966 2358.273812 2366.02324 2370.471418 2371.784879 2375.909149 2379.075768 2385.552289 2389.641257 2389.86403 2390.725414 2395.819447 2407.106186 2408.175752 2429.088981 2434.82175 2440.124058 2467.929505 2471.566345 2477.609812 2489.945091 2491.580836 2510.302527 2511.638942 2515.044217 2515.71574 2517.028608 2524.354087 2524.877922 2526.06623 2535.558951 2535.721585 2536.492167 2537.325694 2537.709872 2539.365845 2540.281295 2553.669496 2558.765222 2571.722037 2572.527088 2574.079989 2582.056893 2582.866113 2588.746023 2591.215869 2599.739431 2608.250835 2609.567659 2609.624241 2610.649666 2619.595179 2621.563198 2636.421069 2639.394226 2642.023428 2669.445731 2675.382826 2680.034969 2694.084625 2696.268191 2704.531319 2707.839333 2716.792796 2730.732111 2732.069684 2737.677226 2778.443114 2782.215594 2785.874988 2791.253987 2791.436181 2801.164946 2805.848603 2806.178239 2810.666466 2813.901345 2830.859735 2853.498852 2856.717434 2868.888741 2870.367911 2878.874877 2883.703936 2885.632474 2905.874322 2907.515485 2916.389413 2923.073962 2929.712875 2931.019447 2933.791531 2933.930571 2935.077087 2949.087886 2976.158269 2981.768596 2983.915934 2989.715087 3001.266498 3002.479299 3011.896758 3011.912842 3020.089709 3025.493187 3042.22217 3043.797986 3044.370623 3045.683447 3053.071336 3054.37784 3059.479072 3063.92801 3107.161718 3151.462292 3173.531554 3256.077437 3256.892854 3259.605488 3265.533651 3289.866956 3319.895402 3333.730546 3378.930836 3388.87019 3396.586658 3408.947451 3436.547313 3439.845643 3464.238962 3465.187872 3465.352967 3498.880213 3522.537563 3533.001912 3553.327338 3600.815901 3603.355282 3717.619142 3755.870625 3760.009184 3773.018383 3783.863253 3811.612746 3811.771546 3814.024695 3867.8375 3883.205185 3886.970119 3908.597606 3911.469436 3917.313319 3939.971862 3986.038094 3987.670344 4046.750127 4066.433675 4092.504341 4097.021961 4169.546188 4186.34036 4187.66536 4190.45858 4204.426559 4212.053484 4243.227528 4250.384263 4254.988812 4260.547742 4262.13353 4320.226864 4367.875314 4388.094236 4536.069442 4564.327703 4567.306728 4604.70907 4646.504789 4671.757915 4699.991441 4787.618259 4876.00814 4904.917446 4917.17524 4925.327908 5011.724579 5098.552384 5303.654354 5308.235953 5309.20718 5392.941707 5419.906687 5475.854044 5495.805466 5568.353474 5640.966362 5729.170599 5756.300338 5806.041122 5832.62891 5835.703258 5915.919707 5927.430005 5981.107598 6030.061892 6096.21919 6167.676768 6201.442974 6225.328106 6260.287897 6289.796644 6322.660099 6326.143981 6469.370471 6476.395452 6499.135987 6644.829529 7103.246168 7336.643496 7450.887574 7463.63355 7724.153705 8125.302957 8315.141568 8480.871611 8667.155784 8993.192971 10269.69361 11689.13634 12457.90885 12725.48318 12877.43593];
%ascending order sort
a = sort(a);
%split into 10.
data = reshape(a,235,10);
%boxplot
boxplot(data(:,1:10))
Produces this:
You can compare the values of a with their quantiles to generate groups with similar sizes. This approch automatically handles the case when the size of a is not a multiple of the number of groups.
N = 10; % desired number of groups
thresholds = quantile(a, 0:1/N:1-1/N); % for approximately equal-size groups
group = sum(bsxfun(#ge, a, thresholds.'), 1); % this ranges from 1 to N
For your example a and data, this gives the following group sizes:
>> sum(bsxfun(#eq, group(:), 1:N), 1)
ans =
235 235 234 235 235 235 235 234 235 235
To do the plot, you probably want to use the average of each group as the x-axis value:
a_average_group = accumarray(group(:), a(:), [], #mean);
boxplot(data, a_average_group(group))
produces
Some way to group the data automatically is to use hist. The elements in a are sorted into 10 equally spaced bins along the x-axis between the minimum and maximum values of a. As hist returns the center of the bins, there is some calulation to get the right border of the bin, so one can calculate the correct bin given some value of a. In your example the 8th bin has no values though, resulting in only 9 boxplots drawn.
a = [0.001818443 0.002518386 0.003220336 0.005121008 0.005896566 0.006067186 0.006398329 0.006398407 0.008139209 0.008179847 0.009156658 0.010554859 0.010897468 0.011446512 0.011646556 0.013025077 0.013744086 0.014617856 0.014830682 0.014941674 0.015810817 0.015953685 0.016022438 0.01662184 0.017497373 0.018051816 0.018084908 0.019634522 0.02055418 0.021050675 0.021573997 0.022819828 0.024124407 0.02499767 0.025745151 0.026366919 0.029380223 0.029534435 0.029988118 0.033517171 0.037612906 0.038975473 0.045889922 0.04598939 0.045995714 0.046398796 0.047502087 0.048214192 0.050098857 0.053184072 0.055892719 0.057670042 0.059855042 0.061763503 0.065288778 0.066057267 0.066842804 0.067799739 0.069549949 0.070151584 0.071572852 0.084351815 0.084676596 0.088092878 0.088329724 0.089958848 0.09000399 0.092272165 0.092561834 0.092888091 0.093193395 0.095380669 0.100645564 0.101506295 0.104656067 0.105057051 0.10527394 0.107024689 0.107608649 0.108699789 0.109433298 0.111805635 0.111990726 0.112119163 0.113570537 0.115032624 0.123301615 0.125012672 0.127758465 0.131636854 0.132397883 0.132910662 0.136185494 0.137039002 0.138037293 0.139764132 0.140727482 0.141044291 0.141523184 0.142202565 0.142563209 0.145192227 0.146881752 0.149407941 0.152043489 0.153144103 0.153181689 0.153795372 0.15548845 0.158134807 0.159905274 0.160042269 0.160098768 0.160483614 0.164710118 0.166850893 0.167909098 0.167986172 0.169019205 0.173698541 0.174264483 0.17696023 0.177973442 0.182348008 0.192484859 0.193880575 0.197216088 0.197939827 0.198109045 0.199497862 0.200821988 0.203783239 0.20515055 0.206573438 0.207185798 0.209268239 0.209386896 0.210951877 0.212023024 0.213363487 0.214391326 0.221532866 0.223109522 0.225343507 0.22641709 0.228343987 0.229206318 0.231114414 0.234752015 0.237026193 0.237716285 0.240721044 0.242380638 0.245407553 0.246434245 0.247559843 0.247849238 0.253027915 0.256495379 0.257901113 0.258536851 0.259306669 0.259722973 0.269672307 0.269866791 0.277255751 0.282296038 0.283236904 0.286946602 0.290243971 0.291043929 0.29129401 0.295039384 0.306007647 0.318012708 0.31816788 0.323432941 0.327635294 0.331093206 0.332249017 0.336175038 0.338095934 0.34029032 0.34317957 0.343788501 0.344121892 0.354931564 0.35667793 0.363077884 0.366887155 0.367370204 0.368269666 0.368423655 0.372253797 0.373533628 0.376022623 0.380837432 0.38261496 0.38391646 0.387548982 0.390389207 0.391454753 0.392089937 0.393426643 0.399546308 0.400245613 0.410202767 0.410776868 0.411723243 0.413456921 0.415884043 0.417789874 0.420313779 0.421429254 0.422511926 0.425439714 0.432455519 0.436032696 0.437367296 0.440152475 0.451378258 0.451755234 0.452195559 0.461256878 0.462486697 0.466794805 0.468274083 0.470176312 0.471656017 0.476690087 0.476869314 0.479601939 0.480684241 0.48433634 0.48590973 0.487749692 0.488207469 0.492988875 0.494796745 0.498156249 0.4985104 0.504612947 0.504967424 0.50679421 0.507507695 0.508506011 0.509330529 0.510055669 0.513319728 0.52891378 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1.172495225 1.175475612 1.176864362 1.17990075 1.182195585 1.189691417 1.19404695 1.201722268 1.207506808 1.207712757 1.217276704 1.226244517 1.231233705 1.235338344 1.235488468 1.237856505 1.246471414 1.248198622 1.24847215 1.249213122 1.253941679 1.255492087 1.261797214 1.272565441 1.275217172 1.279047194 1.2941275 1.296036211 1.303184537 1.303538428 1.304022026 1.306154543 1.313038716 1.325574114 1.325811852 1.329512083 1.335354282 1.336243495 1.349582224 1.349879286 1.349900576 1.351984631 1.352752927 1.380875102 1.381652802 1.385006548 1.385551407 1.399529901 1.400418216 1.400665522 1.402213831 1.407079557 1.407902968 1.426432367 1.427988618 1.430494495 1.436726215 1.455773986 1.457812821 1.457839075 1.464415454 1.465676132 1.471725239 1.478228056 1.493616649 1.505911081 1.510317752 1.517723479 1.519844042 1.520079555 1.52266623 1.528263582 1.528956908 1.539259874 1.54540469 1.552334282 1.559284266 1.560271081 1.560397723 1.58015835 1.584258957 1.586824283 1.5901615 1.601497093 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2.362974205 2.390145632 2.39280766 2.39488483 2.396397248 2.396786702 2.41576171 2.419119918 2.441157923 2.444805269 2.464446901 2.464904486 2.467566697 2.472567888 2.478733367 2.478770886 2.487350756 2.49321811 2.495309064 2.503098264 2.508249701 2.515845321 2.536509644 2.550922635 2.551167925 2.554758102 2.560023347 2.570208707 2.586874641 2.591255858 2.621816341 2.628169732 2.631001714 2.632092126 2.633379668 2.642666194 2.655607509 2.682555117 2.688667321 2.692033844 2.699468672 2.705095951 2.708973091 2.737148617 2.749372431 2.750397407 2.756591892 2.792700138 2.796508678 2.81091224 2.81340476 2.818119651 2.819068516 2.840763732 2.855245731 2.867534546 2.882410894 2.906305385 2.921292046 2.921449315 2.923724053 2.936059643 2.941498451 2.942915417 2.94633052 2.988440975 3.000337153 3.01308498 3.020820735 3.035323578 3.035939809 3.042578546 3.052730332 3.054474708 3.057013298 3.057530131 3.067704681 3.068800023 3.076034658 3.089870019 3.099800517 3.101610913 3.119631497 3.124341072 3.146856917 3.154655951 3.161613073 3.166574531 3.174242956 3.230342089 3.255377691 3.28960399 3.295293223 3.32692446 3.363077516 3.364077103 3.381238529 3.381887576 3.384181674 3.386057784 3.401224741 3.435435804 3.45589793 3.466903497 3.474234492 3.480509064 3.483189477 3.513852304 3.516762563 3.560173959 3.563670036 3.584881386 3.600866595 3.612209011 3.612874405 3.681816583 3.685079285 3.711346042 3.731563626 3.758770274 3.767406987 3.793935874 3.808871162 3.810159575 3.829625617 3.834675737 3.845991621 3.867773548 3.877642315 3.893104221 3.95596396 3.962658928 3.965413469 3.97259321 3.990644309 4.023556719 4.02507181 4.084002607 4.084578556 4.098037962 4.153140723 4.171589196 4.172912226 4.173220429 4.174507202 4.183151827 4.185959555 4.208131209 4.220649959 4.222855241 4.224314464 4.227985127 4.231821774 4.241488572 4.259388079 4.263468946 4.282045733 4.288305453 4.304823313 4.310640075 4.331544589 4.338609003 4.344069461 4.363020386 4.365779429 4.434026574 4.443658027 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11689.13634 12457.90885 12725.48318 12877.43593];
[counts,centers] = hist(a,10);
borders = centers+(centers(2)-centers(1))/2
b = arrayfun(#(x) sum(x>=borders),a);
boxplot(a,b)
I am finding and plotting local maximas and minimas on a waveform with the function findpeaks. I would like to be able to select some of those peaks with a pointer directly from the plot and get the coordinates into the workspace.
Is that possible?
Here is a subset of an example dataset:
data = [-4.89540984473946e-06 -4.89046450287593e-06 -4.88283149336408e-06 -4.87095198116254e-06 -4.90712807029133e-06 -4.91927636347547e-06 -4.84407526018980e-06 -4.87589730823133e-06 -4.92470545237800e-06 -4.84429026519065e-06 -4.94884075275366e-06 -4.97668505593799e-06 -5.05091858731457e-06 -4.91637367843710e-06 -4.84267768702634e-06 -4.89191584860588e-06 -4.84660167647784e-06 -4.83337832656191e-06 -4.79032179995216e-06 -4.76624025231771e-06 -4.75887604778215e-06 -4.70856280330800e-06 -4.64964899691363e-06 -4.72963414438675e-06 -4.83950621114218e-06 -4.81381203536647e-06 -4.83251825696249e-06 -4.87320964430400e-06 -4.90336533160774e-06 -4.83783986967173e-06 -4.80376018565747e-06 -4.74447010720748e-06 -4.84961187054251e-06 -4.83536719945138e-06 -4.79919111834559e-06 -4.85611601862430e-06 -4.82606784644511e-06 -4.85471844872184e-06 -4.84386025701412e-06 -4.81515589140679e-06 -4.83353956282112e-06 -4.88793805514151e-06 -4.79279444702247e-06 -4.81897238803632e-06 -4.80263134571406e-06 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prominence_factor_localp2p = 0.0000004;
figure;
subplot(2,1,1)
findpeaks (data,'MinPeakProminence', prominence_factor_localp2p);
subplot(2,1,2)
findpeaks (-data,'MinPeakProminence', prominence_factor_localp2p);
Try
[x,y] = ginput(1) % Returns x and y after one mouse click
if you want to get one peak or:
[X,Y] = ginput() % Returns X=[x1..xN] and Y=[y1..yN] after N clicks, stop with ENTER
if you want to get multiple peaks in vector format
Unfortunately, I don't have the Signal Processing Toolbox (findpeaks is part of the SPT) so I'll answer this without it.
Say you have a function:
x = sin(linspace(0, 2*pi, 1000)) + 0.1*rand(1, 1000);
plot(linspace(0, 2*pi, 1000),x);
This gives be the following plot:
Now, using the function "Data Cursor" ,
you can select a point on the plot by clicking at it. You can drag the data tip around if you didn't hit the correct point at the first try. If you right click on the frame that comes up, you can export the [x,y] values to the workspace:
This gives the following variable (struct) in the workspace:
peak_value_1
peak_value_1 =
Target: [1x1 Line]
Position: [1.5849 1.0984]
DataIndex: 253
Now you have the positions of the maximum point:
position = peak_value_1.Position
position =
1.5849 1.0984
How you want to proceed from here is your call. Note that you can create multiple data tips by holding the Shift button while clicking.
I'm using MATLAB to plot a colormap of the following data matrix:
1010.89914200000 1006.07847500000 1013.91775300000 1016.37012500000 1012.64447500000 1005.15384200000 998.323644400000 1007.09643600000 1010.39007800000 1010.71070000000 1007.75920300000 1003.43986900000 1001.77407500000 1000.93290600000 1009.19935000000 1010.79651100000 1006.15733600000 1001.08001400000 1006.62765600000 1008.98760600000 1012.59690300000 1014.13669400000 1012.41850000000 1002.83265600000
1010.20939200000 1006.22354400000 1014.13985000000 1016.01628600000 1012.16700600000 1004.47184200000 1000.64279200000 1006.93063300000 1009.98950000000 1010.53409400000 1007.46695300000 1003.07056100000 1001.31949400000 1001.61572800000 1009.12864700000 1010.45935000000 1006.24603900000 1001.40907500000 1006.31782500000 1009.00493300000 1012.13883300000 1013.93106700000 1011.88797800000 1003.01553600000
1009.70062500000 1006.35064700000 1014.37573900000 1015.70891100000 1011.51357800000 1003.79413100000 1001.69951400000 1006.79389200000 1009.72253100000 1010.41560600000 1007.01026100000 1002.91738300000 1000.86708100000 1002.11736400000 1009.09675300000 1009.98591400000 1005.46058600000 1000.94756700000 1006.18716400000 1008.73213100000 1012.14396700000 1013.51734400000 1010.97627200000 1003.29271400000
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1009.26808100000 1006.29626900000 1014.67797500000 1015.67235600000 1011.20240600000 1003.14341400000 1000.42042500000 1006.97025800000 1009.47529700000 1010.42883100000 1006.41034400000 1003.31582500000 1001.22851700000 1003.87331700000 1009.01971100000 1009.26751700000 1004.97643300000 1000.38995600000 1006.25921900000 1008.81359400000 1012.25596700000 1013.53560600000 1010.21200300000 1004.63438300000
1009.71863900000 1006.81711100000 1014.88478900000 1016.09375300000 1011.26197200000 1003.11559200000 1000.71963300000 1007.50932500000 1009.46868300000 1010.70578900000 1006.41695000000 1003.87943100000 1001.47584700000 1004.88967200000 1009.38281100000 1009.50196100000 1005.06519200000 1001.66275800000 1006.49482200000 1009.35829200000 1012.54210000000 1013.92888300000 1009.72913100000 1005.35431100000
1010.74544400000 1007.71330600000 1015.30482800000 1016.41307200000 1011.40723900000 1003.05097800000 1001.88330600000 1007.78023100000 1009.79253600000 1011.11156400000 1006.55320300000 1004.49287800000 1001.40507500000 1005.91256700000 1009.88450300000 1009.38280000000 1004.75816700000 1003.58816900000 1006.68395600000 1009.83212800000 1012.71196100000 1014.27383300000 1009.46791900000 1005.91156100000
1011.06202800000 1008.61448600000 1016.07176900000 1016.77051900000 1011.43023100000 1002.91816900000 1003.01835800000 1008.37585300000 1010.16145800000 1011.46680800000 1006.80956900000 1004.88291700000 1001.64883900000 1006.73743100000 1010.37819200000 1009.44031900000 1005.11520300000 1004.70793600000 1007.29316900000 1010.53632800000 1013.30822200000 1014.53464700000 1009.54564700000 1006.43934700000
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using:
imagesc(data)
Now I need to normalize each column separately in the following way: finding the maximum value of each column and set it's color, and finding the lowest value of each column and set it's color as well.
So in the end I need a colormap which have the same color for every local maximum and minimum of a column.
I'm quite new to plotting, so I tried looking for normalized colormaps here and on the web, but didn't find anything that can help me.
You're not going to be able to do this strictly with a colormap because the colormap is applied globally to the image and the color used for the local max in one column would be applied to all elements with that value regardless of whether they were the local max of their respective column.
Instead, you can normalize each column of your data and then display that.
%// Subtract off the minimum of each column
D = bsxfun(#minus, data, min(data, [], 1));
%// Divide by the maximum
D = bsxfun(#rdivide, D, max(D, [], 1));
%// Display the result
imagesc(D);
I'd like to make one period representative signal.
There are several signals in different periods.
I don't want to cut one period signal from several signals.
How can I solve this problem in matlab?
Below is the signal data values.
Thank you.
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If by "one period representative signal" you mean "get the average signal over all the periods", this is how.
This is what your data looks like:
If you know in advance how many cycles are in your signal (9 for your data above), then just reshape your signal into a 9x178 matrix (==numel(signal)/9) and find column means (ie a 1x178 matrix with the average signal).
nwaves = 9;
sig2 = reshape(sig,[numel(sig)/nwaves nwaves])';
avgSig = mean(sig2,1);
plot(avgSig);
If you don't know how many cycles in your signal...you could try doing the fourier transform, but it's not guaranteed...and if the number of data points isn't an exact multiple of the period length, then you'll have to modify the code to deal with that.
% get fourier transform
a = fft(sig);
% get period with max amplitude (but not period=0)
[val ix] = max(abs(a(2:end))) % don't include period=0
nwaves = ix + 1 % add 1 because of the offset above
% then calculate avg & plot as before with this `nwaves`.
(If you plot each 'cycle' though you'll notice that splitting into every 178 elements doesn't quite section your wave into parts from minimum to minimum - you might need to find a smarter way of doing that).