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In Python fun.txt -0.086107832 0.279109089 1.12109795 0.921406055 0.845488273 1.295830355 1.310718286 1.735717683 1.865533435 2.610081159 2.849604671 2.501217594 2.552051849 2.599851547 3.448864397 3.314477826 3.462990021 4.820334145 2.38704362 2.519236916 2.884221938 2.120354783
In Python
fun.txt
-0.086107832 0.279109089 1.12109795 0.921406055 0.845488273 1.295830355 1.310718286 1.735717683 1.865533435 2.610081159 2.849604671 2.501217594 2.552051849 2.599851547 3.448864397 3.314477826 3.462990021 4.820334145 2.38704362 2.519236916 2.884221938 2.120354783 2.103660078 2.168028349 1.304734504 1.333069614 1.190773098 1.100845576 0.412218164 0.044849118 0.435212177 -0.475335839 -1.0427608 -1.148755123 -0.789322488 -1.128559146 -1.947876788 -2.000293795 -1.78053997 -2.128684422 -2.91398322 -3.007685556 -3.022193246 -3.017536587 -3.411831943 -3.052920883 -3.285814119 -2.645218976 -3.065402131 -2.261376817 -3.060206429 -2.256282254 -2.540310192 -1.798538927 -1.640145863 -1.572483589 -1.373858901 -0.488396418 -0.706629892 1.683153233 -0.245266444 0.474564321 0.34119352 1.249505367 1.146948 1.03581713 2.002415708 2.444151455 1.967420951 2.049631498 2.337816984 2.364526632 2.908585055 3.224012591 2.548947172 3.16701859 3.319472826 3.202280649 2.736463845 2.897752895 2.190311957 2.587453421 2.246005377 2.387676616 1.581343731 1.572148669 1.465105893 0.549928683 0.429586483 0.618909181 -0.277547909 -0.372770192 -1.077048092 -0.675509271 -1.416872049 -1.22860374 -1.612979311 -2.058432551 -1.762287842 -2.202628723 -2.923986261 -3.142393839 -2.414849717 -2.69695442 -2.95537267 -2.898666643 -2.537817211 -2.862775632 -3.216002968 -2.58933555 -2.815880238 -2.298735685 -1.808861214 -2.167423587 -1.512402912 -1.468894734 -1.45268034 -1.232953153 -0.758424456 0.118859696 0.107672779 0.144173689 0.739753504 1.108951113 0.859633153 1.188718095 1.893614396 2.372081406 2.42840503 2.057077708 2.123026217 2.299399275 2.489076608 2.819082882 2.840049695 3.07635439 3.40638042 3.01163765 2.651726977 2.936212314 3.022897063 2.714540328 2.644577708 1.756317217 1.8399997 1.090421766 1.207182034 1.183251192 1.114367977 0.684738624 0.258787487 -0.596393522 -1.015782548 -0.702496287 -1.113970704 -1.681698197 -2.077699608 -1.771570939 -2.549530022 -2.261819688 -2.540679924 -3.240611064 -2.990254278 -2.604728523 -3.343340239 -3.340188802 -2.51919366 -2.963356372 -2.940989071 -3.002741193 -2.382018091 -2.634997207 -2.353808502 -1.671271368 -2.240351967 -1.342999476 -1.709258424 -0.939303514 -0.714211504 -0.057610383 -0.441101047 0.557866574 0.370487762 0.63756146 1.10112681 1.405801583 1.315267524 2.068405931 2.010596752 2.245235951 2.458484037 2.242434926 2.961870563 2.815104314 2.677253852 3.31924752 2.859216668 2.787000519 2.793594126 3.168977728 2.93071452 2.567055694 2.162006783 2.269629376 1.900953644 1.988977081 1.536911029 1.086993038 0.707372899 -0.163072541 -0.346851269 -0.507388479 -0.207294774 -1.259076188 -0.784279638 -1.961170518 -2.151571909 -2.346731884 -2.250566431 -2.284901586 -2.713131165 -2.612723414 -3.031555518 -3.04361327 -2.596802656 -3.059773031 -3.273965604 -3.322818381 -2.450846004 -2.83078703 -2.945416343 -1.982952625 -2.587208279 -2.469363285 -1.987996689 -1.201873431 -0.800039158 -1.190812624 -0.287866792 -0.58107885 0.397415723 0.01728859 0.974310859 1.380615693 -0.536933295 1.138976865 1.598945405 2.108080787 2.524828821 2.782654938 2.377155371 2.504975548 2.821738411 3.387097816 3.306476283 3.47015979 2.717457005 2.64496722 2.924309432 2.623503224 2.673880881 2.700257196 2.438667781 2.157575665 1.956852676 1.322999483 1.599292499 0.661707176 0.207139227 -0.181909098 0.441215956 -0.479458525 -0.658666111 -0.631218082 -1.12979491 -1.362525415 -2.084721699 -1.74249843 -1.788426984 -2.714613565 -2.492533116 -3.062850012 -2.672618878 -3.381482049 -2.849689887 -2.998510004 -3.475143672 -2.8709518 -2.506696244 -2.435703199 -2.483395289 -2.170722455 -2.728481836 -1.91084109 -1.32421357 -1.500142935 -1.334572068 -0.485567997 -0.735548745 -0.752910005 0.010157038 0.406661342 0.428717578 1.178748031 0.944717333 1.403896162 1.889017098 2.457330888 1.749939676 2.548630221 2.398171161 2.240938199 2.556824475 2.867613549 2.958693261 2.975307016 2.729820671 3.377701715 2.528790366 2.278058264 2.964976602 2.290013425 2.33373761 2.025235576 1.709568727 1.604392312 1.149637793 0.749554943 0.426787533 0.798830894 0.102450928 -0.443020499 -0.730823572 -0.618103114 -1.251162253 -1.62425564 -2.050293693 -1.675761879 -2.178754326 -2.262067125 -2.674074356 -2.668280023 -2.741728719 -2.741499226 -3.176710035 -3.3238527 -3.361571983 -3.122207375 -2.62030088 -2.619785223 -2.657345337 -2.63082391 -2.517771112 -2.146102724 -1.2874226 -1.814362042 -1.663435394 -1.394038361 -0.493627919 -0.310793234Using the data in the wavy fun.txt file, do the following: 1. Plot the data 2. See if you find the outliers by examination. 3. See if you can find the outliers by using the standard deviation of the point to point difference of the data. When you find them, identify them, replace them with the average of the surrounding points. 4. Smooth the data using a sliding window. Make window sizes of 5,7 , and 9 points. Try simple average, and weighted average techniques. 5. Calculate your error for each of your sliding windows. The equation is 3sin(6x). Which is best
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