| Previous changeset 0:01212bf66f61 (2018-08-22) Next changeset 2:17c54820f3be (2018-10-25) |
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Commit message:
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/MALDIquant commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415 |
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modified:
maldi_macros.xml maldi_quant_peakdetection.xml test-data/int1.tabular test-data/int2.tabular test-data/intensity_matrix3.tabular test-data/masspeaks2.tabular test-data/masspeaks3.tabular test-data/peakdetection1_QC.pdf test-data/peakdetection2_QC.pdf test-data/peakdetection3_QC.pdf |
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added:
test-data/masspeaks3_forinput.tabular |
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removed:
test-data/masspeaks1_forinput.tabular |
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| diff -r 01212bf66f61 -r eaaa73b043e6 maldi_macros.xml --- a/maldi_macros.xml Wed Aug 22 11:49:29 2018 -0400 +++ b/maldi_macros.xml Mon Oct 01 01:09:43 2018 -0400 |
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| @@ -1,20 +1,28 @@ <macros> <token name="@R_IMPORTS@"><![CDATA[ - ## Libraries library (Cardinal) library (MALDIquantForeign) library (MALDIquant) library (ggplot2) - + library(gridExtra) ]]> </token> + <token name="@MADLI_QUANT_DESCRIPTION@"><![CDATA[ +MALDIquant_ provides a complete analysis pipeline for MALDI-TOF and other mass spectrometry data. +So far we have only implemented the functionalities for mass spectrometry imaging data. + ]]> + </token> + + <token name="@VERSION@">1.18.0</token> + <xml name="requirements"> <requirements> <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> <requirement type="package" version="0.11.5">r-maldiquantforeign</requirement> <requirement type="package" version="1.18">r-maldiquant</requirement> <requirement type="package" version="2.2.1">r-ggplot2</requirement> + <requirement type="package" version="2.2.1">r-gridextra</requirement> </requirements> </xml> |
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| diff -r 01212bf66f61 -r eaaa73b043e6 maldi_quant_peakdetection.xml --- a/maldi_quant_peakdetection.xml Wed Aug 22 11:49:29 2018 -0400 +++ b/maldi_quant_peakdetection.xml Mon Oct 01 01:09:43 2018 -0400 |
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| b'@@ -1,4 +1,4 @@\n-<tool id="maldi_quant_peak_detection" name="MALDIquant peak detection" version="1.18.0.0">\n+<tool id="maldi_quant_peak_detection" name="MALDIquant peak detection" version="@VERSION@.1">\n <description>\n Peak detection, binning and filtering for mass-spectrometry imaging data\n </description>\n@@ -15,6 +15,8 @@\n cp \'${infile.extra_files_path}/hdr\' infile.hdr &&\n cp \'${infile.extra_files_path}/img\' infile.img &&\n cp \'${infile.extra_files_path}/t2m\' infile.t2m &&\n+ #else\n+ ln -s $infile infile.RData &&\n #end if\n Rscript \'${maldi_quant_peak_detection}\'&&\n mkdir $outfile_imzml.files_path &&\n@@ -29,36 +31,39 @@\n \n @R_IMPORTS@\n \n-summarized_spectra = FALSE\n+\n \n #if $restriction_conditional.restriction == \'restrict\':\n \n print(\'Reading mask region\')\n+\n ## Import imzML file\n- coordinate_matrix = as.matrix(read.delim("$restriction_conditional.coordinates_file", header = FALSE, stringsAsFactors = FALSE))[,1:2]\n+ coordinate_matrix = as.matrix(read.delim("$restriction_conditional.coordinates_file", header = $restriction_conditional.coordinates_header, stringsAsFactors = FALSE))[,1:2]\n \n maldi_data <- importImzMl(\'infile.imzML\',\n coordinates = coordinate_matrix, centroided = $centroids)\n- pixelnames = paste0("x = ", coordinates(maldi_data)[,1],", y = ", coordinates(maldi_data)[,2])\n+ pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")\n+\n \n #else:\n \n print(\'Reading entire file\')\n ## Import imzML file\n \n-\n #if $infile.ext == \'imzml\'\n-\n+ print(\'imzML file\')\n #if str($centroids) == "TRUE"\n peaks <- importImzMl(\'infile.imzML\', centroided = $centroids)\n- pixelnames = paste0("x = ", coordinates(peaks)[,1],", y = ", coordinates(peaks)[,2])\n-\n+ pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")\n #else\n maldi_data <- importImzMl(\'infile.imzML\', centroided = $centroids)\n- pixelnames = paste0("x = ", coordinates(maldi_data)[,1],", y = ", coordinates(maldi_data)[,2])\n+ pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")\n #end if\n+ coordinates_info = cbind(coordinates(maldi_data)[,1:2], c(1:length(maldi_data)))\n+\n #elif $infile.ext == \'tabular\'\n-\n+ print(\'tabular file\')\n+ #set $centroids = "TRUE" ## will be used in some if conditions\n peak_tabular = read.delim("$infile", header = TRUE, stringsAsFactors = FALSE)\n peak_list = split(peak_tabular, f = peak_tabular\\$spectrum) ## will be ordered according to spectrum\n pixelnames = unique(peak_tabular\\$spectrum)\n@@ -66,17 +71,55 @@\n peaks = list()\n for (spectra in 1:length(peak_list))\n {\n- single_peaks = createMassPeaks(peak_list[[spectra]]\\$mass, peak_list[[spectra]]\\$intensity, snr=peak_list[[spectra]]\\$snr)\n- peaks[[spectra]] = single_peaks\n+ single_peaks = createMassPeaks(peak_list[[spectra]]\\$mass, peak_list[[spectra]]\\$intensity, snr=peak_list[[spectra]]\\$snr)\n+ peaks[[spectra]] = single_peaks\n }\n \n+ #else\n+ print(\'rdata file\')\n+ loadRData <- function(fileName){\n+ #loads an RData file, and returns it\n+ load(fileName)\n+ get(ls()[ls() != "fileName"])\n+ }\n+ msidata = loadRData(\'infile.RData\')\n+ centroided(msidata) = $centroids\n+ pixelnames = gsub(", y = ", "_", names(Cardinal::pixels(msidata)))\n+ pixelnames = gsub(" = ", "y_", pixelnames)\n+\n+ cardinal_coordinates = as.matrix(Cardinal::coord(msidata)[,1:2])\n+\n+ if (centroided(msidata) == FALSE){\n+ ## create mass spectrum object\n+ cardinal_mzs = Cardinal::mz(msidata)\n+ maldi_data = list()\n+ for(number_spectra in 1:ncol(msidata)){\n+ '..b' on average spectra, further steps will be done on average spectra as well and average spectra are exported. If this option was not chosen the filtering tool can use the annotations to filter for peaks within pixel groups (select "Group wise filtering")\n+ - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_\n+ - Cardinal "MSImageSet" data saved as .RData\n+ - MSI data as peak list (tabular file) with the columns named "snr", "mass", "intensity" and "spectrum". The spectrum has to be in the following format: xy_1_1 (for pixel coordinates x1y1). The header must have exactly the four column names.\n+\n+ :: \n+ \n+ snr mass intensity spectrum\n+ 5.34\t304.16 0.10 xy_1_1\n+ 12.09\t305 0.2 xy_1_1\n+ 6.80\t306.25 0.133 xy_1_1\n+ ...\n+ ... \n \n \n-Options:\n+- Optional: Tabular file with pixel coordinates to restrict reading of imzML files to coordinates of interest. The file has to contain x values in the first column and y values in the second columns. Further columns are allowed. Tabular files with any header name or no header at all are supported. \n+\n+ ::\n+\n+ x_coord y_coord \n+ 1 1 \n+ 2 1 \n+ 3 1 \n+ ...\n+ ...\n+\n+\n+- Optional: Tabular file(s) containing pixel coordinates and annotation. X and y values in separate columns and the corresponding annotation in a third column. Tabular files with any header name or no header at all are supported. The annotations can be used to summarize pixels of an imzML file which belong to the same group and detect peaks on average spectra, further steps will be done on average spectra as well and average spectra are exported. If this option was not chosen the filtering tool can use the annotations to filter for peaks within pixel groups (select "Group wise filtering").\n \n-- Peak detection: detection of peaks, only possible with imzML input\n+ ::\n+\n+ x_coord y_coord annotation\n+ 1 1 healthy\n+ 2 1 healthy\n+ 3 1 disease\n+ ...\n+ ...\n+\n+\n+**Options**\n+\n+- Peak detection: detection of peaks, only possible with profile mode input\n - Monoisotopic peaks: detection of monoisotopic peaks\n-- Peak binning: After the alignment the peak positions (mass) are very similar but not identical. The binning is needed to make similar peak mass values identical.\n+- Peak binning: After the alignment the peak positions (m/z) are very similar but not identical. The binning is needed to make similar peak m/z values identical.\n - Peak filtering: Removal of less frequent peaks (either with a minimum ratio or with an absolute minimum number of spectra in which the peak has to occur)\n \n \n-Output: \n+**Output**\n \n-- centroided processed or continuous imzML file\n-- pdf with mass spectra after each preprocessing step\n+- centroided imzML file (processed or continuous), except for peak picking on the average of multiple spectra\n+- pdf with mass spectra plots after each preprocessing step\n - peak list (tabular file) with the columns "snr", "mass", "intensity" and "spectrum"\n-- tabular file with intensity matrix (m/z in rows and spectra in columns). If the input file was imzML in profile mode the intensities before peak picking are also stored in the matrix . For all other inputs not picked values are set to NA. \n+- tabular file with intensity matrix (m/z in rows and spectra in columns). If the input file was imzML in profile mode the intensities before peak picking are also stored in the matrix . For all other inputs not picked values are set to NA. For peak picking on the average of multiple spectra, each spectra group is a column with mean intensities for each m/z\n \n .. _MALDIquant: http://strimmerlab.org/software/maldiquant/\n \n' |
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| diff -r 01212bf66f61 -r eaaa73b043e6 test-data/int1.tabular --- a/test-data/int1.tabular Wed Aug 22 11:49:29 2018 -0400 +++ b/test-data/int1.tabular Mon Oct 01 01:09:43 2018 -0400 |
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| @@ -1,4 +1,4 @@ -mz | spectra col1 col2 col3 +mz col1 col2 col3 300.083343505859 0 0.0878976186116536 0 304 9.438736829163e-11 0.137228806813558 0.203098436196645 304.166687011719 0.104457606871923 0.0169352528949116 0.00765286510187286 |
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| diff -r 01212bf66f61 -r eaaa73b043e6 test-data/int2.tabular --- a/test-data/int2.tabular Wed Aug 22 11:49:29 2018 -0400 +++ b/test-data/int2.tabular Mon Oct 01 01:09:43 2018 -0400 |
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| @@ -0,0 +1,10 @@ +mz xy_1_1 xy_2_1 xy_3_1 xy_1_2 xy_2_2 xy_3_2 xy_1_3 xy_2_3 xy_3_3 +308.266668701172 NA NA NA 0.399492412805557 2.67954346782062e-05 0.630028009414673 NA NA NA +329.152786254883 2.47849035263062 1.6380660533905 0.519839584827423 NA NA NA NA NA NA +329.697925567627 NA NA NA NA 2.56914954661625e-05 NA 4.62229545519222e-05 NA 6.18351987213828e-05 +343.642865862165 NA 0.662226140499115 NA NA NA NA NA NA NA +344.611124674479 0.638258039951324 NA NA NA NA NA NA NA NA +366.562515258789 NA 2.64647725956024e-09 2.61009136011126e-05 NA NA NA NA NA NA +367.645835876465 0.235070616006851 NA NA NA NA NA NA NA NA +387.850006103516 NA NA NA NA NA NA 2.8698808819172e-05 0.197833687067032 4.42154814663809e-05 +394.791679382324 NA NA NA NA NA NA NA 1.05953142589779e-21 0.310508847236633 |
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| diff -r 01212bf66f61 -r eaaa73b043e6 test-data/intensity_matrix3.tabular --- a/test-data/intensity_matrix3.tabular Wed Aug 22 11:49:29 2018 -0400 +++ b/test-data/intensity_matrix3.tabular Mon Oct 01 01:09:43 2018 -0400 |
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| @@ -1,4 +1,4 @@ -mz | spectra x = 1, y = 1 x = 2, y = 1 x = 3, y = 1 x = 1, y = 2 x = 2, y = 2 x = 3, y = 2 x = 1, y = 3 x = 2, y = 3 x = 3, y = 3 +mz xy_1_1 xy_2_1 xy_3_1 xy_1_2 xy_2_2 xy_3_2 xy_1_3 xy_2_3 xy_3_3 304.595254080636 1.76512676262064e-05 0 0.327420804051165 1.17177887401856e-13 2.65423709869989e-05 0 0.279548346996307 0.256212419994475 0.226718190873036 305.119057791574 1.62699820283052e-09 0 1.36897748140363e-05 5.69736818276873e-18 1.71781823336396e-09 0 0.716848015785217 1.44706262158933e-05 9.11607707771881e-06 305.66667175293 1.02237303638199e-13 0 0 5.6757509669499e-22 1.38465480219223e-13 0 6.01442479819525e-05 0.104363307437671 0 |
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| diff -r 01212bf66f61 -r eaaa73b043e6 test-data/masspeaks1_forinput.tabular --- a/test-data/masspeaks1_forinput.tabular Wed Aug 22 11:49:29 2018 -0400 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -1,99 +0,0 @@ -snr mass intensity spectrum -5.34196717869329 304.166687011719 0.104457606871923 x = 1, y = 1 -12.0922480027988 305 0.238949338595072 x = 1, y = 1 -6.80380283473404 306.25 0.133792817751516 x = 1, y = 1 -11.9239943102966 306.666687011719 0.232789536317284 x = 1, y = 1 -16.7672721943661 306.916687011719 0.322907487551371 x = 1, y = 1 -14.5727405573344 315.916687011719 0.134740673005581 x = 1, y = 1 -12.6587975530526 316.25 0.117780188719432 x = 1, y = 1 -15.5891513371263 320.916687011719 0.0873032609621684 x = 1, y = 1 -8.28066653191209 322.75 0.107258200645447 x = 1, y = 1 -8.64305080588796 327.083343505859 0.593451981743177 x = 1, y = 1 -22.5976205263238 329 1.80600585540136 x = 1, y = 1 -6.62986417925206 343.083343505859 0.319934318463008 x = 1, y = 1 -5.76872573536526 343.75 0.314895849686906 x = 1, y = 1 -7.23012875909998 344 0.41009783744812 x = 1, y = 1 -12.5923211364761 345 0.777459700902303 x = 1, y = 1 -5.61154187261139 346.083343505859 0.311080597071943 x = 1, y = 1 -11.6128628366043 353.083343505859 0.0853602389494578 x = 1, y = 1 -8.93926402677033 355.166687011719 0.093819797039032 x = 1, y = 1 -11.4518402499656 355.916687011719 0.124494592348735 x = 1, y = 1 -12.2687590055812 356.916687011719 0.131409923235575 x = 1, y = 1 -17.1598526473503 360.916687011719 0.159461249907811 x = 1, y = 1 -19.5606964350667 364.333343505859 0.279228508472443 x = 1, y = 1 -13.4363791597757 365.583343505859 0.189359684795294 x = 1, y = 1 -7.2659142337533 366.083343505859 0.0986319063122271 x = 1, y = 1 -6.78882554537553 367.583343505859 0.0783568720022837 x = 1, y = 1 -19.2913990161673 370.083343505859 0.149130453666051 x = 1, y = 1 -22.9258781818056 374.5 0.10046042005221 x = 1, y = 1 -32.553974512381 395.916687011719 0.170605540275574 x = 1, y = 1 -20.0044568991975 397.083343505859 0.0962067842483521 x = 1, y = 1 -52.5251008144488 398.75 0.214157521724701 x = 1, y = 1 -6.53932310180994 300.083343505859 0.0878976186116536 x = 2, y = 1 -8.1332053348611 304 0.137228806813558 x = 2, y = 1 -8.87376047659255 304.666687011719 0.15201666439801 x = 2, y = 1 -9.0031548908286 305.75 0.155222187439613 x = 2, y = 1 -12.1483037475036 305.916687011719 0.20923868753016 x = 2, y = 1 -5.50393277232579 306.916687011719 0.0935481031580518 x = 2, y = 1 -8.92694410124806 307.083343505859 0.151374835480731 x = 2, y = 1 -8.53366295883023 307.75 0.14276859164238 x = 2, y = 1 -20.8363523568967 308.25 0.34317347407341 x = 2, y = 1 -9.03952553159105 311.5 0.126103460788727 x = 2, y = 1 -9.21792692425819 313.166687011719 0.11177615324661 x = 2, y = 1 -10.2836388640006 314 0.114434331655502 x = 2, y = 1 -14.5366950159814 315.833343505859 0.110506186882656 x = 2, y = 1 -29.6030745455647 321.25 0.0977287689844767 x = 2, y = 1 -5.12133326721085 327.916687011719 0.406224122348552 x = 2, y = 1 -18.2260181460723 328.916687011719 1.60846790671349 x = 2, y = 1 -6.71691695849357 339.416687011719 0.214046796162923 x = 2, y = 1 -5.42044735697872 343.916687011719 0.369042471672098 x = 2, y = 1 -9.90109899068147 344.916687011719 0.751941124598185 x = 2, y = 1 -5.7761050136785 346 0.407967170079549 x = 2, y = 1 -9.53380778579935 350.083343505859 0.103204836448034 x = 2, y = 1 -22.3739163562105 357.333343505859 0.132146000862122 x = 2, y = 1 -17.1123310736305 359.916687011719 0.0798314611117045 x = 2, y = 1 -21.5296474065392 365.833343505859 0.247531970342 x = 2, y = 1 -17.6598326813686 367.916687011719 0.225095887978872 x = 2, y = 1 -14.4139559364593 369.083343505859 0.167913814385732 x = 2, y = 1 -13.4612234580055 370.416687011719 0.129989445209503 x = 2, y = 1 -10.0270809962262 376 0.0839582482973735 x = 2, y = 1 -13.7957864133576 378.75 0.090022732814153 x = 2, y = 1 -21.7167106106545 382.583343505859 0.111637224753698 x = 2, y = 1 -37.803872450968 383.166687011719 0.194509585698446 x = 2, y = 1 -19.3443105357995 386.75 0.0984363655249278 x = 2, y = 1 -40.2946953152003 393.583343505859 0.136588126420975 x = 2, y = 1 -10.9042784289401 304 0.203098436196645 x = 3, y = 1 -14.2536155186416 304.666687011719 0.269343495368958 x = 3, y = 1 -9.73428286587393 305.916687011719 0.186377684291186 x = 3, y = 1 -25.326392266369 307.166687011719 0.48292171999357 x = 3, y = 1 -5.77081370276002 308 0.107646773258845 x = 3, y = 1 -9.71607704287468 309.833343505859 0.161644687255224 x = 3, y = 1 -12.3286648153297 314.75 0.168087442715963 x = 3, y = 1 -10.0188114825306 314.916687011719 0.136831253767014 x = 3, y = 1 -9.98544088935621 318.083343505859 0.131532917420069 x = 3, y = 1 -15.9567928309838 318.416687011719 0.202652255694071 x = 3, y = 1 -5.45721434599321 326.916687011719 0.25384642680486 x = 3, y = 1 -6.94450239587698 328 0.340036729971568 x = 3, y = 1 -26.3506501037786 328.916687011719 1.28705859184265 x = 3, y = 1 -21.9755116973922 329.083343505859 1.05529999732971 x = 3, y = 1 -6.26698769334215 342.333343505859 0.19008461634318 x = 3, y = 1 -7.40453698942461 343.916687011719 0.2857492963473 x = 3, y = 1 -5.47629632143471 344.333343505859 0.225824773311615 x = 3, y = 1 -24.2489760518392 345.083343505859 1.09834182262421 x = 3, y = 1 -7.55360848799134 346 0.326915502548218 x = 3, y = 1 -31.8141872712877 355.333343505859 0.218277891476949 x = 3, y = 1 -28.500421447754 359.916687011719 0.0977983176708221 x = 3, y = 1 -35.9263674883478 370.333343505859 0.318489452203115 x = 3, y = 1 -17.9023349585808 376.833343505859 0.119955976804097 x = 3, y = 1 -14.165837350632 379.5 0.138252675533295 x = 3, y = 1 -6.1026956808649 380.75 0.063951442639033 x = 3, y = 1 -6.66805606030542 386.666687011719 0.0939600268999736 x = 3, y = 1 -10.4761700777795 387 0.148616979519526 x = 3, y = 1 -14.9996173987781 387.166687011719 0.213376681009928 x = 3, y = 1 -9.49389038421466 387.5 0.135613699754079 x = 3, y = 1 -6.40597511255643 390.166687011719 0.0942935446898143 x = 3, y = 1 -6.91093965563744 391.083343505859 0.103488087654114 x = 3, y = 1 -12.1864531063779 392 0.184977193673452 x = 3, y = 1 -17.1091639921622 392.833343505859 0.264233609040578 x = 3, y = 1 -6.41767571506743 394.833343505859 0.103502949078878 x = 3, y = 1 -8.23619853479686 398.166687011719 0.142847468455633 x = 3, y = 1 |
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| diff -r 01212bf66f61 -r eaaa73b043e6 test-data/masspeaks2.tabular --- a/test-data/masspeaks2.tabular Wed Aug 22 11:49:29 2018 -0400 +++ b/test-data/masspeaks2.tabular Mon Oct 01 01:09:43 2018 -0400 |
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| @@ -0,0 +1,20 @@ +snr mass intensity spectrum +Inf 329.152786254883 2.47849035263062 xy_1_1 +Inf 344.611124674479 0.638258039951324 xy_1_1 +Inf 367.645835876465 0.235070616006851 xy_1_1 +Inf 329.152786254883 1.6380660533905 xy_2_1 +Inf 343.642865862165 0.662226140499115 xy_2_1 +Inf 366.562515258789 2.64647725956024e-09 xy_2_1 +Inf 329.152786254883 0.519839584827423 xy_3_1 +Inf 366.562515258789 2.61009136011126e-05 xy_3_1 +Inf 308.266668701172 0.399492412805557 xy_1_2 +Inf 308.266668701172 2.67954346782062e-05 xy_2_2 +Inf 329.697925567627 2.56914954661625e-05 xy_2_2 +Inf 308.266668701172 0.630028009414673 xy_3_2 +Inf 329.697925567627 4.62229545519222e-05 xy_1_3 +Inf 387.850006103516 2.8698808819172e-05 xy_1_3 +Inf 387.850006103516 0.197833687067032 xy_2_3 +Inf 394.791679382324 1.05953142589779e-21 xy_2_3 +Inf 329.697925567627 6.18351987213828e-05 xy_3_3 +Inf 387.850006103516 4.42154814663809e-05 xy_3_3 +Inf 394.791679382324 0.310508847236633 xy_3_3 |
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| diff -r 01212bf66f61 -r eaaa73b043e6 test-data/masspeaks3.tabular --- a/test-data/masspeaks3.tabular Wed Aug 22 11:49:29 2018 -0400 +++ b/test-data/masspeaks3.tabular Mon Oct 01 01:09:43 2018 -0400 |
| b |
| b'@@ -1,298 +1,298 @@\n snr\tmass\tintensity\tspectrum\n-Inf\t304.595254080636\t1.76512676262064e-05\tx = 1, y = 1\n-Inf\t305.119057791574\t1.62699820283052e-09\tx = 1, y = 1\n-Inf\t305.66667175293\t1.02237303638199e-13\tx = 1, y = 1\n-Inf\t306.32292175293\t7.71168242810122e-18\tx = 1, y = 1\n-Inf\t307.031265258789\t5.54882387838857e-22\tx = 1, y = 1\n-Inf\t307.614593505859\t3.61475171997316e-26\tx = 1, y = 1\n-Inf\t308.718757629395\t1.67789168240286e-34\tx = 1, y = 1\n-Inf\t309.250011444092\t1.43981749664202e-38\tx = 1, y = 1\n-Inf\t321.050012207031\t1.5556590571819e-38\tx = 1, y = 1\n-Inf\t328.000010172526\t0.481155782938004\tx = 1, y = 1\n-Inf\t329.152786254883\t2.47849035263062\tx = 1, y = 1\n-Inf\t330.197929382324\t1.09462394703996e-08\tx = 1, y = 1\n-Inf\t330.712968614366\t5.96421675068404e-13\tx = 1, y = 1\n-Inf\t331.259270562066\t5.44180783738833e-17\tx = 1, y = 1\n-Inf\t331.796308729384\t2.72964412068832e-21\tx = 1, y = 1\n-Inf\t332.342600504557\t2.65403450832136e-25\tx = 1, y = 1\n-Inf\t332.879638671875\t1.38079261482617e-29\tx = 1, y = 1\n-Inf\t333.44445461697\t1.27447841383671e-33\tx = 1, y = 1\n-Inf\t343.642865862165\t0.282461315393448\tx = 1, y = 1\n-Inf\t344.611124674479\t0.638258039951324\tx = 1, y = 1\n-Inf\t345.629638671875\t7.93658109614626e-05\tx = 1, y = 1\n-Inf\t346.120378282335\t6.64322641341641e-09\tx = 1, y = 1\n-Inf\t346.700012207031\t3.66650991252132e-13\tx = 1, y = 1\n-Inf\t347.296308729384\t3.31281302377731e-17\tx = 1, y = 1\n-Inf\t347.814826117622\t1.67992344863529e-21\tx = 1, y = 1\n-Inf\t348.361117892795\t1.61966283306326e-25\tx = 1, y = 1\n-Inf\t348.888895670573\t8.15391718679523e-30\tx = 1, y = 1\n-Inf\t349.44445461697\t7.79337969080295e-34\tx = 1, y = 1\n-Inf\t349.96297539605\t4.78329195057699e-38\tx = 1, y = 1\n-Inf\t365.979179382324\t3.88570062627783e-13\tx = 1, y = 1\n-Inf\t366.562515258789\t1.91497258346089e-17\tx = 1, y = 1\n-Inf\t367.083343505859\t1.88767583715038e-21\tx = 1, y = 1\n-Inf\t367.645835876465\t0.235070616006851\tx = 1, y = 1\n-Inf\t368.222229003906\t1.47346945595928e-05\tx = 1, y = 1\n-Inf\t368.666680908203\t1.13727949280218e-09\tx = 1, y = 1\n-Inf\t369.216674804688\t6.86236075017783e-14\tx = 1, y = 1\n-Inf\t369.766680908203\t5.79879376075753e-18\tx = 1, y = 1\n-Inf\t370.523821149554\t2.65765684162034e-05\tx = 1, y = 1\n-Inf\t306.32292175293\t1.93815667444142e-05\tx = 2, y = 1\n-Inf\t307.031265258789\t1.36712752230039e-09\tx = 2, y = 1\n-Inf\t307.614593505859\t9.10052605105835e-14\tx = 2, y = 1\n-Inf\t308.266668701172\t0.399492412805557\tx = 2, y = 1\n-Inf\t308.718757629395\t2.19716112042079e-05\tx = 2, y = 1\n-Inf\t309.250011444092\t1.93809390758304e-09\tx = 2, y = 1\n-Inf\t309.833347865513\t1.00940015081239e-13\tx = 2, y = 1\n-Inf\t310.361124674479\t9.53307623430011e-18\tx = 2, y = 1\n-Inf\t310.902786254883\t4.60080996999485e-22\tx = 2, y = 1\n-Inf\t311.430557250977\t0.37831038236618\tx = 2, y = 1\n-Inf\t311.986119588216\t3.04782115563285e-05\tx = 2, y = 1\n-Inf\t312.516674804688\t1.61912494522909e-09\tx = 2, y = 1\n-Inf\t328.937515258789\t2.02454543113708\tx = 2, y = 1\n-Inf\t329.697925567627\t4.36672562500462e-05\tx = 2, y = 1\n-Inf\t330.197929382324\t0.291347920894623\tx = 2, y = 1\n-Inf\t330.712968614366\t2.65860908257309e-05\tx = 2, y = 1\n-Inf\t331.259270562066\t1.33338984298348e-09\tx = 2, y = 1\n-Inf\t331.796308729384\t0.256167501211166\tx = 2, y = 1\n-Inf\t332.342600504557\t1.61519819812384e-05\tx = 2, y = 1\n-Inf\t332.879638671875\t0.473990738391876\tx = 2, y = 1\n-Inf\t333.44445461697\t3.11978510580957e-05\tx = 2, y = 1\n-Inf\t334.020843505859\t2.16568318833765e-09\tx = 2, y = 1\n-Inf\t343.642865862165\t4.07043262384832e-05\tx = 2, y = 1\n-Inf\t345.629638671875\t0.388675004243851\tx = 2, y = 1\n-Inf\t346.120378282335\t1.22390151023865\tx = 2, y = 1\n-Inf\t347.296308729384\t2.53151047218125e-05\tx = 2, y = 1\n-Inf\t347.814826117622\t2.07963690712631e-09\tx = 2, y = 1\n-Inf\t348.361117892795\t1.17175312421697e-13\tx = 2, y = 1\n-Inf\t348.888895670573\t1.04223425420895e-17\tx = 2, y = 1\n-Inf\t349.44445461697\t5.37785487481536e-22\tx = 2, y = 1\n-Inf\t349.96297539605\t0.309614509344101\tx = 2, y = 1\n-Inf\t351.069458007812\t1.2832610529756e-09\tx = 2, y = 1\n-Inf\t370.523821149554\t2.16230963798365e-13\tx = 2, y = 1\n-Inf\t328.937515258789\t1.4315873'..b'4\txy_1_3\n+Inf\t343.642865862165\t2.33215705520706e-05\txy_1_3\n+Inf\t344.611124674479\t1.14931461158048e-13\txy_1_3\n+Inf\t345.629638671875\t2.49274271482136e-05\txy_1_3\n+Inf\t346.120378282335\t2.42154163387909e-09\txy_1_3\n+Inf\t346.700012207031\t1.25566657765974e-13\txy_1_3\n+Inf\t347.296308729384\t1.16314638763216e-17\txy_1_3\n+Inf\t347.814826117622\t7.28580269542504e-22\txy_1_3\n+Inf\t348.361117892795\t5.51452352978133e-26\txy_1_3\n+Inf\t348.888895670573\t3.95872379808908e-30\txy_1_3\n+Inf\t349.44445461697\t2.58541051461361e-34\txy_1_3\n+Inf\t349.96297539605\t2.00786591889092e-38\txy_1_3\n+Inf\t365.979179382324\t0.29585200548172\txy_1_3\n+Inf\t366.562515258789\t2.61009136011126e-05\txy_1_3\n+Inf\t367.083343505859\t1.29202726295574e-09\txy_1_3\n+Inf\t367.645835876465\t1.26925610321495e-13\txy_1_3\n+Inf\t368.222229003906\t6.86202649112451e-18\txy_1_3\n+Inf\t368.666680908203\t6.0804923995715e-22\txy_1_3\n+Inf\t369.216674804688\t3.92168363149906e-26\txy_1_3\n+Inf\t369.766680908203\t2.87628364894794e-30\txy_1_3\n+Inf\t370.523821149554\t2.11079916033934e-34\txy_1_3\n+Inf\t306.32292175293\t2.58914169535274e-05\txy_2_3\n+Inf\t307.031265258789\t0.2786685526371\txy_2_3\n+Inf\t307.614593505859\t2.96689468086697e-05\txy_2_3\n+Inf\t308.266668701172\t0.630028009414673\txy_2_3\n+Inf\t308.718757629395\t3.5055691114394e-05\txy_2_3\n+Inf\t309.250011444092\t3.4764433554102e-09\txy_2_3\n+Inf\t309.833347865513\t2.08417769262566e-13\txy_2_3\n+Inf\t310.361124674479\t1.65365266219334e-17\txy_2_3\n+Inf\t310.902786254883\t1.14908291585848e-21\txy_2_3\n+Inf\t311.430557250977\t7.77481833182232e-26\txy_2_3\n+Inf\t311.986119588216\t6.04272858040201e-30\txy_2_3\n+Inf\t312.516674804688\t3.61853642833949e-34\txy_2_3\n+Inf\t328.937515258789\t1.3272477388382\txy_2_3\n+Inf\t329.697925567627\t0.000119319425721187\txy_2_3\n+Inf\t330.197929382324\t0.87813526391983\txy_2_3\n+Inf\t330.712968614366\t5.94691991864238e-05\txy_2_3\n+Inf\t331.259270562066\t4.48100578864796e-09\txy_2_3\n+Inf\t331.796308729384\t2.77607174444319e-13\txy_2_3\n+Inf\t332.342600504557\t2.30068624134988e-17\txy_2_3\n+Inf\t332.879638671875\t1.28379127790181e-21\txy_2_3\n+Inf\t333.44445461697\t1.15039499526719e-25\txy_2_3\n+Inf\t334.020843505859\t5.88708673366388e-30\txy_2_3\n+Inf\t343.642865862165\t0.628214061260223\txy_2_3\n+Inf\t345.629638671875\t2.52073332376312e-05\txy_2_3\n+Inf\t346.120378282335\t2.42913511527831e-09\txy_2_3\n+Inf\t347.296308729384\t3.06663314404432e-05\txy_2_3\n+Inf\t347.814826117622\t1.64383595624429e-09\txy_2_3\n+Inf\t348.361117892795\t1.4698648114625e-13\txy_2_3\n+Inf\t348.888895670573\t9.42402156952173e-18\txy_2_3\n+Inf\t349.44445461697\t6.95620575449764e-22\txy_2_3\n+Inf\t349.96297539605\t5.08223484114241e-26\txy_2_3\n+Inf\t351.069458007812\t2.63284561467787e-34\txy_2_3\n+Inf\t370.523821149554\t0.38996833562851\txy_2_3\n+Inf\t328.937515258789\t1.43404459953308\txy_3_3\n+Inf\t329.697925567627\t6.18351987213828e-05\txy_3_3\n+Inf\t330.712968614366\t3.4482751940304e-13\txy_3_3\n+Inf\t331.259270562066\t2.93395139921047e-17\txy_3_3\n+Inf\t331.796308729384\t1.94369975414076e-21\txy_3_3\n+Inf\t332.342600504557\t1.38486306848585e-25\txy_3_3\n+Inf\t332.879638671875\t1.03717248235199e-29\txy_3_3\n+Inf\t333.44445461697\t6.46787389371651e-34\txy_3_3\n+Inf\t334.020843505859\t5.33374608969737e-38\txy_3_3\n+Inf\t345.629638671875\t0.000100398268841673\txy_3_3\n+Inf\t346.120378282335\t0.700047612190247\txy_3_3\n+Inf\t347.296308729384\t0.290130436420441\txy_3_3\n+Inf\t347.814826117622\t1.69061459018849e-05\txy_3_3\n+Inf\t348.361117892795\t1.38276745609289e-09\txy_3_3\n+Inf\t348.888895670573\t9.4092654945744e-14\txy_3_3\n+Inf\t349.44445461697\t6.51254681822392e-18\txy_3_3\n+Inf\t349.96297539605\t4.97919259365601e-22\txy_3_3\n+Inf\t387.850006103516\t4.42154814663809e-05\txy_3_3\n+Inf\t388.895843505859\t2.20461995013854e-13\txy_3_3\n+Inf\t389.854179382324\t1.07776605300705e-21\txy_3_3\n+Inf\t391.083335876465\t5.18557991695755e-30\txy_3_3\n+Inf\t391.972229003906\t2.46211811583847e-38\txy_3_3\n+Inf\t394.083351135254\t0.225453585386276\txy_3_3\n+Inf\t394.791679382324\t0.310508847236633\txy_3_3\n+Inf\t395.270843505859\t2.11353399208747e-05\txy_3_3\n+Inf\t395.816680908203\t1.37501721120259e-09\txy_3_3\n+Inf\t396.383337402344\t9.99066823194339e-14\txy_3_3\n+Inf\t396.916674804687\t7.3794850397387e-18\txy_3_3\n' |
| b |
| diff -r 01212bf66f61 -r eaaa73b043e6 test-data/masspeaks3_forinput.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/masspeaks3_forinput.tabular Mon Oct 01 01:09:43 2018 -0400 |
| b |
| b'@@ -0,0 +1,298 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