What\'s the best MATLAB function to use to get the smoothest, most accurate curv

Question

What's the best MATLAB function to use to get the smoothest, most accurate curve and remove the noise from this set of data? (Please provide code and plot the curves) I need the BEST function because I need to have certain, clear, and distinct peaks. Thanks.

Data with Random Noise Generated:

0.0286
-0.1235
0.4010
0.0747
0.2815
-0.1137
-0.1665
-0.1814
0.0967
0.0850
-0.1449
-0.3425
-0.2378
0.0034
0.1291
0.3039
0.1203
0.1306
-0.0319
-0.1242
-0.3406
0.0659
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-0.4602
0.2101
-0.0140
0.0703
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0.0223
0.3909
0.1157
-0.1244
-0.0518
-0.1956
0.1899
-0.0688
0.2332
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0.1933
0.1787
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0.2043
0.0461
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0.0354
0.0320
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0.0547
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0.0550
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0.1590
0.0252
0.2431
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0.1205
0.0676
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0.0890
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0.0380
0.1228
0.1734
0.4329
0.1940
0.0841
0.0994
0.1431
0.1094
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-0.2921
0.0208
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0.0108
0.3264
0.2634
0.4061
-0.0030
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0.3119
0.1774
0.1342
-0.1114
0.1338
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0.1051
0.0169
0.0642
0.4522
0.5221
0.0908
0.1014
0.3239
0.3504
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0.0895
0.0330
0.3200
0.4697
0.1850
0.3615
0.1300
0.1845
0.4889
0.3026
0.3663
0.4237
0.0838
0.4158
0.1349
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0.4929
0.3294
0.3816
0.1658
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0.1390
0.3410
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0.2262
0.3838
0.4625
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0.0693
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1.0562
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1.2827
1.4189
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0.9705
1.1432
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0.1145
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0.1236
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0.0395
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0.1325
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0.0538
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0.1946
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0.3014
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0.1851
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0.1089
0.1132
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0.1611
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0.0836
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0.1026
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0.0782
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0.3768
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0.1179
0.1283
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0.2624
0.1310
0.2435
-0.0498
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0.1726
0.2026
0.1959
0.2650
0.1120
0.0862
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0.1335
0.2225
0.3495
0.0102
0.2285
0.2812
0.3504
0.1433
-0.0145
0.1090
0.0519
0.1951
-0.0799
0.0999
0.1204
0.3664
0.4120
0.3414
0.1439
0.1660
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0.5167
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0.4484
0.1128
0.3306
0.2897
0.4763
0.5916
0.3112
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0.1224
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0.5014
0.4903
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0.1221
0.5508
0.6352
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0.4062
0.6287
0.4669
0.4534
0.5568
0.3271
0.3953
0.2475
0.1886
0.4052
0.6204
0.3973
0.6290
0.3297
0.4603
0.3516
0.5619
0.5833
0.3902
0.3355
-0.0205
0.2261
0.6338
0.8324
0.5502
0.4823
0.5662
0.1795
0.6498
0.4928
0.3095
0.4518
0.4571
0.4578
0.6008
0.4495
0.4417
0.6667
0.0105
0.4684
0.1412
0.4233
0.6490
0.0713
0.3787
0.2155
0.4871
0.3881
0.1134
0.4217
0.4452
0.1303
0.1844
0.5219
0.2068
0.4145
0.5104
0.4033
0.4754
0.1425
0.3673
0.4677
0.3956
0.5012
-0.1217
0.3068
0.1912
0.1919
0.3450
0.1359
0.2350
-0.0354
0.1423
0.2041
0.1041
0.0114
0.2570
0.3535
0.1521
-0.1425
0.0633
0.0352
-0.3954
-0.0083
0.1468
0.1548
-0.3260
-0.0259
0.1603
-0.0978
0.1280
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0.1662
0.2459
0.1421
-0.0312
-0.0079
-0.1117
-0.0765
0.1088
0.0144
0.0263
0.1513
0.1016

Example Text file of what the data should be close to (it looks like an EKG)

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0.0000000e+00
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0.0000000e+00
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0.0000000e+00
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0.0000000e+00
-0.0000000e+00
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1.1000000e-03
2.6000000e-03
4.0000000e-03
5.6000000e-03
7.2000000e-03
8.8000000e-03
1.0600000e-02
1.2500000e-02
1.4500000e-02
1.6600000e-02
1.8800000e-02
2.1100000e-02
2.3500000e-02
2.6100000e-02
2.8800000e-02
3.1600000e-02
3.4600000e-02
3.7700000e-02
4.0900000e-02
4.4300000e-02
4.8000000e-02
5.1700000e-02
5.5600000e-02
5.9800000e-02
6.4000000e-02
6.8500000e-02
7.3100000e-02
7.7900000e-02
8.3000000e-02
8.8200000e-02
9.3700000e-02
9.9300000e-02
1.0510000e-01
1.1120000e-01
1.1740000e-01
1.2390000e-01
1.3050000e-01
1.3730000e-01
1.4430000e-01
1.5140000e-01
1.5880000e-01
1.6640000e-01
1.7420000e-01
1.8210000e-01
1.9000000e-01
1.9820000e-01
2.0660000e-01
2.1510000e-01
2.2360000e-01
2.3220000e-01
2.4100000e-01
2.4990000e-01
2.5870000e-01
2.6770000e-01
2.7660000e-01
2.8560000e-01
2.9460000e-01
3.0360000e-01
3.1250000e-01
3.2130000e-01
3.3010000e-01
3.3880000e-01
3.4740000e-01
3.5580000e-01
3.6410000e-01
3.7220000e-01
3.8010000e-01
3.8780000e-01
3.9520000e-01
4.0230000e-01
4.0930000e-01
4.1580000e-01
4.2200000e-01
4.2790000e-01
4.3340000e-01
4.3860000e-01
4.4330000e-01
4.4770000e-01
4.5170000e-01
4.5520000e-01
4.5830000e-01
4.6100000e-01
4.6320000e-01
4.6500000e-01
4.6630000e-01
4.6730000e-01
4.6760000e-01
4.6990000e-01
4.7360000e-01
4.7650000e-01
4.7920000e-01
4.8120000e-01
4.8300000e-01
4.8410000e-01
4.8480000e-01
4.8520000e-01
4.8440000e-01
4.8380000e-01
4.8240000e-01
4.8060000e-01
4.7840000e-01
4.7570000e-01
4.7270000e-01
4.6910000e-01
4.6520000e-01
4.6090000e-01
4.5610000e-01
4.5110000e-01
4.4580000e-01
4.4010000e-01
4.3410000e-01
4.2770000e-01
4.2110000e-01
4.1420000e-01
4.0700000e-01
3.9960000e-01
3.9190000e-01
3.8400000e-01
3.7590000e-01
3.6770000e-01
3.5930000e-01
3.5060000e-01
3.4180000e-01
3.3290000e-01
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3.1480000e-01
3.0550000e-01
2.9620000e-01
2.8680000e-01
2.7740000e-01
2.6790000e-01
2.5850000e-01
2.4900000e-01
2.3960000e-01
2.3020000e-01
2.2080000e-01
2.1150000e-01
2.0230000e-01
1.9320000e-01
1.8430000e-01
1.7540000e-01
1.6680000e-01
1.5830000e-01
1.5000000e-01
1.4180000e-01
1.3390000e-01
1.2620000e-01
1.1850000e-01
1.1120000e-01
1.0400000e-01
9.6900000e-02
9.0100000e-02
8.3300000e-02
7.6900000e-02
7.0600000e-02
6.4500000e-02
5.8600000e-02
5.2800000e-02
4.7200000e-02
4.1700000e-02
3.6500000e-02
3.1500000e-02
2.6600000e-02
2.1700000e-02
1.7200000e-02
1.2700000e-02
8.5000000e-03
4.3000000e-03
6.0000000e-04
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0.0000000e+00
-1.0000000e-04
0.0000000e+00
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0.0000000e+00
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0.0000000e+00
-0.0000000e+00
0.0000000e+00

Explanation / Answer

% declare y= data+noise , what value you have given

yy = smooth(y,'sgolay',3) % this will give you smooth plot

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