Repository 'maldi_quant_preprocessing'
hg clone https://toolshed.g2.bx.psu.edu/repos/galaxyp/maldi_quant_preprocessing

Changeset 2:e754c2b545a9 (2018-10-25)
Previous changeset 1:0892a051eb17 (2018-10-01) Next changeset 3:71411ac28268 (2019-02-15)
Commit message:
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/MALDIquant commit d2f311f7fff24e54c565127c40414de708e31b3c
modified:
maldi_macros.xml
maldi_quant_preprocessing.xml
test-data/Preprocessing1_QC.pdf
test-data/Preprocessing2_QC.pdf
test-data/Preprocessing3_QC.pdf
test-data/outfile1.ibd
test-data/outfile1.imzML
test-data/outfile2.ibd
test-data/outfile2.imzML
test-data/outfile3.ibd
test-data/outfile3.imzML
test-data/peakdetection1_QC.pdf
test-data/peakdetection2_QC.pdf
test-data/peakdetection3_QC.pdf
added:
test-data/intensity_matrix4.tabular
test-data/masspeaks4.tabular
test-data/peakdetection4_QC.pdf
test-data/testfile_squares.rdata
b
diff -r 0892a051eb17 -r e754c2b545a9 maldi_macros.xml
--- a/maldi_macros.xml Mon Oct 01 01:09:28 2018 -0400
+++ b/maldi_macros.xml Thu Oct 25 07:31:55 2018 -0400
b
@@ -29,6 +29,7 @@
     <xml name="citation">
     <citations>
         <citation type="doi">10.1093/bioinformatics/bts447</citation>
+        <citation type="doi">10.1007/978-3-319-45809-0_6</citation>
     </citations>
     </xml>
 </macros>
b
diff -r 0892a051eb17 -r e754c2b545a9 maldi_quant_preprocessing.xml
--- a/maldi_quant_preprocessing.xml Mon Oct 01 01:09:28 2018 -0400
+++ b/maldi_quant_preprocessing.xml Thu Oct 25 07:31:55 2018 -0400
[
b'@@ -1,4 +1,4 @@\n-<tool id="maldi_quant_preprocessing" name="MALDIquant preprocessing" version="@VERSION@.1">\r\n+<tool id="maldi_quant_preprocessing" name="MALDIquant preprocessing" version="@VERSION@.2">\r\n     <description>\r\n         Preprocessing of mass-spectrometry imaging data\r\n     </description>\r\n@@ -8,6 +8,7 @@\n     <expand macro="requirements"/>\r\n     <command detect_errors="exit_code">\r\n     <![CDATA[\r\n+        cat \'${maldi_quant_preprocessing}\' &&\r\n         #if $infile.ext == \'imzml\'\r\n             cp \'${infile.extra_files_path}/imzml\' infile.imzML &&\r\n             cp \'${infile.extra_files_path}/ibd\' infile.ibd &&\r\n@@ -22,6 +23,7 @@\n             ln -s $infile infile.RData &&\r\n         #end if\r\n         Rscript "${maldi_quant_preprocessing}" &&\r\n+\r\n         mkdir $outfile_imzml.files_path &&\r\n         mv ./out.imzMl "${os.path.join($outfile_imzml.files_path, \'imzml\')}" | true &&\r\n         mv ./out.ibd "${os.path.join($outfile_imzml.files_path, \'ibd\')}" | true &&\r\n@@ -54,7 +56,7 @@\n         coordinates_info = cbind(coordinates(maldi_data)[,1:2], c(1:length(maldi_data)))\r\n     #elif $infile.ext == \'analyze75\'\r\n         ## Import analyze7.5 file\r\n-        maldi_data = import( \'infile.hdr\' )\r\n+        maldi_data = importAnalyze( \'infile.hdr\' )\r\n         coordinates_info = cbind(coordinates(maldi_data)[,1:2], c(1:length(maldi_data)))\r\n     #else\r\n         loadRData <- function(fileName){\r\n@@ -142,10 +144,11 @@\n pixel_number = length(maldi_data)\r\n minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4)\r\n maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4)\r\n-maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)\r\n+mean_features = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)\r\n+number_features = length(unique(unlist(lapply(maldi_data,mass))))\r\n medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2)\r\n-inputdata = c(minmz, maxmz,maxfeatures,  medint)\r\n-QC_numbers= data.frame(inputdata = c(minmz, maxmz,maxfeatures, medint))\r\n+inputdata = c(minmz, maxmz,number_features,mean_features,  medint)\r\n+QC_numbers= data.frame(inputdata = c(minmz, maxmz,number_features, mean_features, medint))\r\n vectorofactions = "inputdata"\r\n \r\n \r\n@@ -162,9 +165,10 @@\n         pixel_number = length(maldi_data)\r\n         minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4)\r\n         maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4)\r\n-        maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)\r\n+        mean_features = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)\r\n         medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2)\r\n-        transformed = c(minmz, maxmz,maxfeatures, medint)\r\n+        number_features = length(unique(unlist(lapply(maldi_data,mass))))\r\n+        transformed = c(minmz, maxmz,number_features,mean_features,  medint)\r\n         QC_numbers= cbind(QC_numbers, transformed)\r\n         vectorofactions = append(vectorofactions, "transformed")\r\n \r\n@@ -196,9 +200,10 @@\n         pixel_number = length(maldi_data)\r\n         minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4)\r\n         maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4)\r\n-        maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)\r\n+        mean_features = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)\r\n         medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2)\r\n-        smoothed = c(minmz, maxmz,maxfeatures, medint)\r\n+        number_features = length(unique(unlist(lapply(maldi_data,mass))))\r\n+        smoothed = c(minmz, maxmz,number_features,mean_features,  medint)\r\n         QC_numbers= cbind(QC_numbers, smoothed)\r\n         vectorofactions = append(vectorofactions, "smoothed")\r\n \r\n@@ -251,9 +256,10 @@\n         pixel_number = length(maldi_data)\r\n         minmz = round(min(unlist('..b'               <param name="tolerance" value="0.002"/>\r\n                 <param name="allow_nomatch" value="TRUE"/>\r\n                 <param name="remove_empty" value="TRUE"/>\r\n                 <param name="empty_nomatch" value="TRUE"/>\r\n@@ -580,7 +593,6 @@\n             <output name="outfile_imzml" file="outfile3.imzML" compare="sim_size"/>\r\n             <output name="outfile_imzml" file="outfile3.ibd" compare="sim_size"/>\r\n             <output name="plots" file="Preprocessing3_QC.pdf" compare="sim_size"/>\r\n-            <output name="annotation_output" file="annotations_output3.tabular"/>\r\n         </test>\r\n     </tests>\r\n     <help><![CDATA[\r\n@@ -623,17 +635,26 @@\n \r\n **Options**\r\n \r\n-- Transformation: transformation of intensities with log, log2, log10 and squareroot\r\n-- Smoothing: Smoothing of the peaks reduces noise and improves peak detection. Available smoothing methods are SavitzkyGolay and Moving Average\r\n+- Transformation: Variance stabilization through intensity transformation:\'log\', \'log2\', \'log10\' and \'squareroot\' (sqrt) are available\r\n+- Smoothing: Smoothing of the peaks reduces noise and improves peak detection. Available smoothing methods are \'SavitzkyGolay\' and \'Moving Average\'\r\n+\r\n+    - For all smoothing methods: The larger the \'Half window size\'f, the stronger the smoothing. The resulting window should be smaller than the FWHM (full width at half maximum) of the typical peaks. Moving average needs smaller window size than SavitzkyGolay.\r\n+    - Moving average: Recommended for broader peaks/high m/z range spectra. Weighted moving average: Points in the center get larger weight factors than points away from the center.  \r\n+    - SavitzkyGolay: Recommended for sharp peaks/low m/z range, preserves the shape of the local maxima. The PolynomialOrder should be smaller than the resulting window. Negative values will be replaced with 0. \r\n+\r\n - Baseline reduction: Baseline reduction removes background intensity generated by chemical noise (common in MALDI datasets). \r\n \r\n     - Available methods are SNIP, TopHat,ConvexHull and median:\r\n     - SNIP is the default baseline reduction method in MALDIquant. \r\n-    - ConvexHull cannot be used for MALDI-TOF baseline removal. \r\n+    - ConvexHull is not appropriate for MALDI-TOF baseline removal. \r\n     - The moving median may generate negative intensities. \r\n     - Except for the ConvexHull all methods have a parameter for the \'Half window size\' (in SNIP it is called \'iterations\'). The smaller the window the more baseline will be removed but also parts of the peaks. Wider windows preserve the peak height better and produce a smoother baseline, but some local background variation will remain. \r\n \r\n - Intensity calibration (normalization): Normalization of intensities to Total Ion Current (TIC), median spectrum, Probabilistic Quotient Normalization (PQN)\r\n+\r\n+    - TIC and median are local calibration methods: each spectrum is normalized on its own (each peak is divided by the TIC or median of the spectrum)\r\n+    - PQN is a global calibration method: In PQN all spectra are calibrated using the TIC calibration first. Subsequently, a median reference spectrum is created and the intensities in all spectra are standardized using the reference spectrum and a spectrum-specific median is calculated for each spectrum. Finally, each spectrum is rescaled by the median of the ratios of its intensity values and that of the reference spectrum\r\n+\r\n - Spectra alignment (warping): alignment for (re)calibration of m/z values, at least two m/z per spectrum are needed for the alignment. This requirement can be skipped by setting "Don\'t throw an error when less than 2 reference m/z were found in a spectrum" to yes. If the not aligned spectra should be set to zero select yes in "logical, if TRUE the intensity values of MassSpectrum or MassPeaks objects with missing (NA) warping functions are set to zero". In order to remove such empty spectra set "Should empty spectra be removed" to yes. \r\n \r\n \r\n'
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/Preprocessing1_QC.pdf
b
Binary file test-data/Preprocessing1_QC.pdf has changed
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/Preprocessing2_QC.pdf
b
Binary file test-data/Preprocessing2_QC.pdf has changed
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/Preprocessing3_QC.pdf
b
Binary file test-data/Preprocessing3_QC.pdf has changed
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/intensity_matrix4.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/intensity_matrix4.tabular Thu Oct 25 07:31:55 2018 -0400
b
b'@@ -0,0 +1,751 @@\n+mz\txy_1_1\txy_2_1\txy_3_1\txy_4_1\txy_1_2\txy_2_2\txy_3_2\txy_4_2\txy_1_3\txy_2_3\txy_3_3\txy_4_3\txy_10_1\txy_11_1\txy_12_1\txy_13_1\txy_10_2\txy_11_2\txy_12_2\txy_13_2\txy_10_3\txy_11_3\txy_12_3\txy_13_3\n+900.398864746094\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t26\t27\t13\tNA\tNA\tNA\t17\t25\t37\t21\t49\t36\t28\n+900.434692382812\tNA\t31\t70\t54\t34\t39\t40\t63\t67\tNA\t32\tNA\tNA\tNA\t15\t13\t20\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+900.470520019531\t32\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t23\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+901.223266601562\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+901.36669921875\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t10\tNA\tNA\tNA\tNA\tNA\tNA\n+901.402526855469\t23\tNA\tNA\tNA\tNA\tNA\tNA\t32\tNA\tNA\tNA\t18\t19\tNA\t6\t10\tNA\tNA\t16\t13\t8\t26\t18\t26\n+901.438415527344\tNA\t22\t39\t28\t19\t26\t30\tNA\t39\t15\t22\tNA\tNA\tNA\tNA\tNA\t12\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+902.335083007812\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t10\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+902.370910644531\t17\tNA\tNA\t10\t9\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t9\tNA\tNA\tNA\tNA\t8\tNA\tNA\t9\n+902.406799316406\tNA\t7\tNA\tNA\tNA\t10\t12\t15\tNA\tNA\t12\tNA\tNA\t5\tNA\tNA\tNA\tNA\t8\t11\tNA\t8\tNA\tNA\n+902.442687988281\tNA\tNA\t15\tNA\tNA\tNA\tNA\tNA\t17\t7\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+903.232177734375\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+903.303955078125\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\t8\tNA\t10\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\t9\t6\n+903.33984375\tNA\t10\tNA\t7\t8\tNA\t7\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\t6\t5\tNA\t6\tNA\tNA\tNA\n+903.375732421875\tNA\tNA\t11\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\t7\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+903.41162109375\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\tNA\n+903.447570800781\t10\tNA\tNA\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+904.165649414062\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\n+904.237426757812\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+904.273376464844\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t9\tNA\n+904.309265136719\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\t8\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\t5\n+904.34521484375\tNA\t6\tNA\tNA\t6\tNA\tNA\t10\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+904.381103515625\t9\tNA\tNA\t9\tNA\tNA\t6\tNA\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+904.417053222656\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+905.063659667969\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+905.135498046875\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\t7\tNA\tNA\t5\tNA\tNA\tNA\n+905.207397460938\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+905.279235839844\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\t8\tNA\tNA\tNA\tNA\n+905.315185546875\t11\tNA\t8\tNA\tNA\t6\t7\t8\t10\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t9\tNA\n+905.351135253906\tNA\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\n+905.387023925781\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+906.357543945312\tNA\tNA\t92\tNA\t73\tNA\t130\t143\tNA\t41\t75\t43\t105\t82\t91\t59\t99\t73\t93\t113\t106\t125\t119\t77\n+906.3935546875\t133\t50\tNA\t76\tNA\t72\tNA\tNA\t82\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+907.32861328125\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t48\t43\tNA\tNA\t28\t60\tNA\tNA\tNA\tNA\t38\n+907.364624023438\t84\tNA\t59\t47\t41\tNA\t54\t67\t40\t33\tNA\t28\t61\tNA\tNA\t28\t44\tNA\tNA\t64\t51\t66\t66\tNA\n+907.400573730469\tNA\t32\tNA\tNA\tNA\t48\tNA\tNA\tNA\tNA\t31\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+908.336181640625\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t17\tNA\tNA\t13\t18\t16\tNA\tNA\t14\t12\tNA\tNA\t19\t20\tNA\t20\n+908.372192382812\t27\tNA\tNA\tNA\t12\t24\t20\t23\tNA\t13\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t16\t22\tNA\tNA\t20\tNA\n+908.408203125\tNA\t8\t19\t17\tNA\tNA\tNA\tNA\tNA\tNA\t16\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+909.272277832031\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t16\tNA\tNA\tNA\t16\tNA\tNA\t17\tNA\t12\t10\tNA\tNA\tNA\tNA\t11\n+909.308288574219\tNA\t8\t10\tNA\tNA\t19\t16\t16\tNA\t10\tNA\t13\tNA\t14\tNA\tNA\tNA\tNA\tNA\t13\tNA\t14\t27\tNA\n+909.344299316406\t23\tNA\tNA\t13\t13\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t11\tNA\t9\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+909.380310058594\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t14\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n'..b'\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+995.306884765625\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+995.344543457031\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t4\tNA\tNA\tNA\tNA\n+995.382263183594\tNA\tNA\t8\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+995.419921875\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\t4\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+995.457580566406\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\t10\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+995.64599609375\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\n+996.211364746094\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+996.249084472656\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\n+996.286743164062\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+996.324462890625\tNA\tNA\tNA\tNA\t5\tNA\t8\tNA\t6\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+996.362182617188\t7\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+996.399841308594\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\n+996.437561035156\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+996.475280761719\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\n+996.550659179688\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+996.626098632812\tNA\t4\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+997.153991699219\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+997.191711425781\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+997.229431152344\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\n+997.267150878906\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t4\tNA\tNA\tNA\tNA\tNA\n+997.304870605469\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\n+997.342590332031\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+997.380249023438\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\n+997.41796875\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+997.455688476562\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+998.059326171875\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\n+998.097045898438\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+998.134765625\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t4\tNA\tNA\tNA\tNA\n+998.172485351562\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+998.247985839844\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+998.285705566406\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+998.323425292969\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\t6\n+998.361206054688\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+998.39892578125\t5\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+998.436645507812\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t11\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\n+998.474365234375\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t4\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+999.116027832031\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+999.15380859375\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+999.191528320312\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\n+999.267028808594\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\n+999.304809570312\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\n+999.342529296875\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\tNA\t6\tNA\tNA\tNA\tNA\tNA\t8\tNA\tNA\t4\tNA\tNA\tNA\n+999.380310058594\tNA\tNA\tNA\tNA\tNA\tNA\t8\tNA\t7\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\t5\tNA\n+999.418090820312\tNA\tNA\t9\tNA\tNA\t8\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n+999.455810546875\t7\tNA\tNA\t6\tNA\tNA\tNA\t7\tNA\tNA\tNA\tNA\t4\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\tNA\n'
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/masspeaks4.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/masspeaks4.tabular Thu Oct 25 07:31:55 2018 -0400
b
b'@@ -0,0 +1,1862 @@\n+snr\tmass\tintensity\tspectrum\n+21.583704303251\t900.470520019531\t32\txy_1_1\n+15.5132874679617\t901.402526855469\t23\txy_1_1\n+11.4663429111021\t902.370910644531\t17\txy_1_1\n+6.74490759476595\t903.447570800781\t10\txy_1_1\n+6.07041683528936\t904.381103515625\t9\txy_1_1\n+7.41939835424255\t905.315185546875\t11\txy_1_1\n+89.7072710103872\t906.3935546875\t133\txy_1_1\n+56.657223796034\t907.364624023438\t84\txy_1_1\n+18.2112505058681\t908.372192382812\t27\txy_1_1\n+15.5132874679617\t909.344299316406\t23\txy_1_1\n+11.4663429111021\t910.352966308594\t17\txy_1_1\n+6.07041683528936\t911.290100097656\t9\txy_1_1\n+21.583704303251\t912.877136230469\t32\txy_1_1\n+10.7918521516255\t913.887756347656\t16\txy_1_1\n+11.4663429111021\t916.344543457031\t17\txy_1_1\n+18.2112505058681\t917.140075683594\t27\txy_1_1\n+8.76837987319574\t918.333984375\t13\txy_1_1\n+6.07041683528936\t919.166625976562\t9\txy_1_1\n+4.04694455685957\t920.289428710938\t6\txy_1_1\n+4.04694455685957\t921.086669921875\t6\txy_1_1\n+5.39592607581276\t922.138061523438\t8\txy_1_1\n+4.04694455685957\t923.843444824219\t6\txy_1_1\n+5.39592607581276\t925.296020507812\t8\txy_1_1\n+5.39592607581276\t927.404296875\t8\txy_1_1\n+31.0265749359234\t928.350158691406\t46\txy_1_1\n+18.2112505058681\t929.369323730469\t27\txy_1_1\n+7.41939835424255\t932.320739746094\t11\txy_1_1\n+12.1408336705787\t933.378601074219\t18\txy_1_1\n+10.1173613921489\t934.364013671875\t15\txy_1_1\n+9.44287063267233\t935.386474609375\t14\txy_1_1\n+6.74490759476595\t936.372924804688\t10\txy_1_1\n+6.07041683528936\t937.359924316406\t9\txy_1_1\n+8.09388911371914\t938.310852050781\t12\txy_1_1\n+11.4663429111021\t939.335510253906\t17\txy_1_1\n+6.74490759476595\t940.397277832031\t10\txy_1_1\n+7.41939835424255\t941.386413574219\t11\txy_1_1\n+4.04694455685957\t942.33935546875\t6\txy_1_1\n+7.41939835424255\t943.439575195312\t11\txy_1_1\n+20.9092135437745\t944.356872558594\t31\txy_1_1\n+18.2112505058681\t945.384765625\t27\txy_1_1\n+5.39592607581276\t947.33203125\t8\txy_1_1\n+6.07041683528936\t948.361511230469\t9\txy_1_1\n+6.74490759476595\t949.318054199219\t10\txy_1_1\n+4.04694455685957\t950.385437011719\t6\txy_1_1\n+6.07041683528936\t951.342956542969\t9\txy_1_1\n+3.37245379738298\t952.227294921875\t5\txy_1_1\n+6.74490759476595\t953.296325683594\t10\txy_1_1\n+6.74490759476595\t954.439758300781\t10\txy_1_1\n+7.41939835424255\t955.399353027344\t11\txy_1_1\n+8.09388911371914\t956.396301269531\t12\txy_1_1\n+6.07041683528936\t957.319885253906\t9\txy_1_1\n+8.09388911371914\t958.428771972656\t12\txy_1_1\n+6.74490759476595\t959.390319824219\t10\txy_1_1\n+5.39592607581276\t960.352355957031\t8\txy_1_1\n+10.1173613921489\t961.388977050781\t15\txy_1_1\n+6.74490759476595\t962.389038085938\t10\txy_1_1\n+5.39592607581276\t963.352600097656\t8\txy_1_1\n+5.39592607581276\t964.353698730469\t8\txy_1_1\n+4.72143531633617\t965.318237304688\t7\txy_1_1\n+4.72143531633617\t966.357482910156\t7\txy_1_1\n+6.07041683528936\t967.322998046875\t9\txy_1_1\n+3.37245379738298\t968.400512695312\t5\txy_1_1\n+5.39592607581276\t969.329895019531\t8\txy_1_1\n+3.37245379738298\t970.371276855469\t5\txy_1_1\n+4.04694455685957\t971.450500488281\t6\txy_1_1\n+5.39592607581276\t972.344055175781\t8\txy_1_1\n+4.72143531633617\t973.424377441406\t7\txy_1_1\n+4.04694455685957\t974.31884765625\t6\txy_1_1\n+4.04694455685957\t975.400207519531\t6\txy_1_1\n+6.07041683528936\t976.332946777344\t9\txy_1_1\n+6.07041683528936\t977.303466796875\t9\txy_1_1\n+4.72143531633617\t978.311767578125\t7\txy_1_1\n+5.39592607581276\t979.395385742188\t8\txy_1_1\n+4.72143531633617\t980.367370605469\t7\txy_1_1\n+4.72143531633617\t981.377319335938\t7\txy_1_1\n+4.04694455685957\t982.425170898438\t6\txy_1_1\n+4.04694455685957\t983.323791503906\t6\txy_1_1\n+4.04694455685957\t984.41015625\t6\txy_1_1\n+4.72143531633617\t985.384643554688\t7\txy_1_1\n+4.72143531633617\t986.284606933594\t7\txy_1_1\n+6.74490759476595\t987.41015625\t10\txy_1_1\n+6.07041683528936\t988.348571777344\t9\txy_1_1\n+5.39592607581276\t990.414733886719\t8\txy_1_1\n+4.72143531633617\t991.505004882812\t7\txy_1_1\n+3.37245379738298\t993.009826660156\t5\txy_1_1\n+4.04694455685957\t994.327514648438\t6\txy_1_1\n+5.39592607581276\t995.344543457031\t8\txy_1_1\n+4.72143531633617\t996.362182617188\t7\txy_1_1\n+3.37245379738298\t998.39892578125\t5\txy_1_1\n+4.72143531633617\t999.45'..b'+4.72143531633617\t962.31494140625\t7\txy_12_3\n+6.74490759476595\t964.76171875\t10\txy_12_3\n+4.72143531633617\t966.283203125\t7\txy_12_3\n+4.72143531633617\t967.285888671875\t7\txy_12_3\n+5.39592607581276\t969.292663574219\t8\txy_12_3\n+4.72143531633617\t970.817749023438\t7\txy_12_3\n+4.04694455685957\t972.567504882812\t6\txy_12_3\n+4.04694455685957\t975.13916015625\t6\txy_12_3\n+8.76837987319574\t976.295654296875\t13\txy_12_3\n+5.39592607581276\t977.26611328125\t8\txy_12_3\n+6.07041683528936\t979.245849609375\t9\txy_12_3\n+4.04694455685957\t980.330017089844\t6\txy_12_3\n+5.39592607581276\t981.339904785156\t8\txy_12_3\n+4.04694455685957\t983.810729980469\t6\txy_12_3\n+4.04694455685957\t986.772277832031\t6\txy_12_3\n+4.04694455685957\t988.273498535156\t6\txy_12_3\n+4.04694455685957\t989.325073242188\t6\txy_12_3\n+3.37245379738298\t990.377136230469\t5\txy_12_3\n+3.37245379738298\t991.655456542969\t5\txy_12_3\n+4.04694455685957\t993.461486816406\t6\txy_12_3\n+3.37245379738298\t996.249084472656\t5\txy_12_3\n+4.04694455685957\t997.380249023438\t6\txy_12_3\n+4.04694455685957\t998.436645507812\t6\txy_12_3\n+3.37245379738298\t999.380310058594\t5\txy_12_3\n+18.8857412653447\t900.398864746094\t28\txy_13_3\n+17.5367597463915\t901.402526855469\t26\txy_13_3\n+6.07041683528936\t902.370910644531\t9\txy_13_3\n+4.04694455685957\t903.303955078125\t6\txy_13_3\n+3.37245379738298\t904.309265136719\t5\txy_13_3\n+51.9357884796978\t906.357543945312\t77\txy_13_3\n+25.6306488601106\t907.32861328125\t38\txy_13_3\n+13.4898151895319\t908.336181640625\t20\txy_13_3\n+7.41939835424255\t909.272277832031\t11\txy_13_3\n+4.72143531633617\t910.316955566406\t7\txy_13_3\n+31.0265749359234\t912.841064453125\t46\txy_13_3\n+12.1408336705787\t913.851684570312\t18\txy_13_3\n+5.39592607581276\t914.862854003906\t8\txy_13_3\n+12.1408336705787\t916.308349609375\t18\txy_13_3\n+10.1173613921489\t917.140075683594\t15\txy_13_3\n+7.41939835424255\t918.116882324219\t11\txy_13_3\n+3.37245379738298\t919.56494140625\t5\txy_13_3\n+4.72143531633617\t921.304138183594\t7\txy_13_3\n+3.37245379738298\t925.332336425781\t5\txy_13_3\n+3.37245379738298\t926.240783691406\t5\txy_13_3\n+4.04694455685957\t927.040588378906\t6\txy_13_3\n+30.3520841764468\t928.8232421875\t45\txy_13_3\n+19.5602320248213\t929.842712402344\t29\txy_13_3\n+8.09388911371914\t930.826232910156\t12\txy_13_3\n+4.04694455685957\t934.291015625\t6\txy_13_3\n+4.04694455685957\t935.313415527344\t6\txy_13_3\n+4.04694455685957\t936.263305664062\t6\txy_13_3\n+5.39592607581276\t938.237731933594\t8\txy_13_3\n+3.37245379738298\t940.140930175781\t5\txy_13_3\n+4.04694455685957\t941.239868164062\t6\txy_13_3\n+4.04694455685957\t942.302734375\t6\txy_13_3\n+10.1173613921489\t944.79736328125\t15\txy_13_3\n+4.04694455685957\t947.258483886719\t6\txy_13_3\n+4.04694455685957\t948.839721679688\t6\txy_13_3\n+4.72143531633617\t950.311828613281\t7\txy_13_3\n+4.04694455685957\t951.121948242188\t6\txy_13_3\n+4.72143531633617\t953.406982421875\t7\txy_13_3\n+3.37245379738298\t954.734985351562\t5\txy_13_3\n+4.72143531633617\t955.546997070312\t7\txy_13_3\n+5.39592607581276\t958.354858398438\t8\txy_13_3\n+5.39592607581276\t959.427307128906\t8\txy_13_3\n+3.37245379738298\t961.314880371094\t5\txy_13_3\n+3.37245379738298\t963.3896484375\t5\txy_13_3\n+4.04694455685957\t965.244018554688\t6\txy_13_3\n+3.37245379738298\t969.627380371094\t5\txy_13_3\n+4.04694455685957\t972.344055175781\t6\txy_13_3\n+4.04694455685957\t973.349853515625\t6\txy_13_3\n+4.72143531633617\t974.467956542969\t7\txy_13_3\n+4.72143531633617\t975.362915039062\t7\txy_13_3\n+4.04694455685957\t976.221008300781\t6\txy_13_3\n+4.04694455685957\t978.311767578125\t6\txy_13_3\n+5.39592607581276\t979.320617675781\t8\txy_13_3\n+5.39592607581276\t980.330017089844\t8\txy_13_3\n+3.37245379738298\t982.125732421875\t5\txy_13_3\n+3.37245379738298\t983.398681640625\t5\txy_13_3\n+2.69796303790638\t984.635009765625\t4\txy_13_3\n+2.69796303790638\t986.097106933594\t4\txy_13_3\n+4.04694455685957\t987.335083007812\t6\txy_13_3\n+4.04694455685957\t989.550476074219\t6\txy_13_3\n+3.37245379738298\t991.392211914062\t5\txy_13_3\n+3.37245379738298\t993.197998046875\t5\txy_13_3\n+3.37245379738298\t994.628784179688\t5\txy_13_3\n+3.37245379738298\t996.399841308594\t5\txy_13_3\n+4.04694455685957\t998.323425292969\t6\txy_13_3\n+3.37245379738298\t999.304809570312\t5\txy_13_3\n'
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/outfile1.ibd
--- a/test-data/outfile1.ibd Mon Oct 01 01:09:28 2018 -0400
+++ b/test-data/outfile1.ibd Thu Oct 25 07:31:55 2018 -0400
b
@@ -1,4 +1,4 @@
 imzML file:
 total 84
--rw-r--r-- 1 meli meli 67160 Aug 22 13:56 ibd
--rw-r--r-- 1 meli meli 15071 Aug 22 13:56 imzml
+-rw-r--r-- 1 meli meli 67160 Okt 24 10:12 ibd
+-rw-r--r-- 1 meli meli 15071 Okt 24 10:12 imzml
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/outfile1.imzML
--- a/test-data/outfile1.imzML Mon Oct 01 01:09:28 2018 -0400
+++ b/test-data/outfile1.imzML Thu Oct 25 07:31:55 2018 -0400
b
@@ -1,4 +1,4 @@
 imzML file:
 total 84
--rw-r--r-- 1 meli meli 67160 Aug 22 13:56 ibd
--rw-r--r-- 1 meli meli 15071 Aug 22 13:56 imzml
+-rw-r--r-- 1 meli meli 67160 Okt 24 10:12 ibd
+-rw-r--r-- 1 meli meli 15071 Okt 24 10:12 imzml
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/outfile2.ibd
--- a/test-data/outfile2.ibd Mon Oct 01 01:09:28 2018 -0400
+++ b/test-data/outfile2.ibd Thu Oct 25 07:31:55 2018 -0400
b
@@ -1,4 +1,4 @@
 imzML file:
 total 276
--rw-r--r-- 1 meli meli 268784 Aug 22 13:56 ibd
--rw-r--r-- 1 meli meli   9286 Aug 22 13:56 imzml
+-rw-r--r-- 1 meli meli 268784 Okt 24 10:12 ibd
+-rw-r--r-- 1 meli meli   9286 Okt 24 10:12 imzml
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/outfile2.imzML
--- a/test-data/outfile2.imzML Mon Oct 01 01:09:28 2018 -0400
+++ b/test-data/outfile2.imzML Thu Oct 25 07:31:55 2018 -0400
b
@@ -1,4 +1,4 @@
 imzML file:
 total 276
--rw-r--r-- 1 meli meli 268784 Aug 22 13:56 ibd
--rw-r--r-- 1 meli meli   9286 Aug 22 13:56 imzml
+-rw-r--r-- 1 meli meli 268784 Okt 24 10:12 ibd
+-rw-r--r-- 1 meli meli   9286 Okt 24 10:12 imzml
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/outfile3.ibd
--- a/test-data/outfile3.ibd Mon Oct 01 01:09:28 2018 -0400
+++ b/test-data/outfile3.ibd Thu Oct 25 07:31:55 2018 -0400
b
@@ -1,4 +1,4 @@
 imzML file:
 total 52
--rw-r--r-- 1 meli meli 38384 Aug 22 13:57 ibd
--rw-r--r-- 1 meli meli  9551 Aug 22 13:57 imzml
+-rw-r--r-- 1 meli meli 38384 Okt 24 10:13 ibd
+-rw-r--r-- 1 meli meli  9551 Okt 24 10:13 imzml
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/outfile3.imzML
--- a/test-data/outfile3.imzML Mon Oct 01 01:09:28 2018 -0400
+++ b/test-data/outfile3.imzML Thu Oct 25 07:31:55 2018 -0400
b
@@ -1,4 +1,4 @@
 imzML file:
 total 52
--rw-r--r-- 1 meli meli 38384 Aug 22 13:57 ibd
--rw-r--r-- 1 meli meli  9551 Aug 22 13:57 imzml
+-rw-r--r-- 1 meli meli 38384 Okt 24 10:13 ibd
+-rw-r--r-- 1 meli meli  9551 Okt 24 10:13 imzml
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/peakdetection1_QC.pdf
b
Binary file test-data/peakdetection1_QC.pdf has changed
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/peakdetection2_QC.pdf
b
Binary file test-data/peakdetection2_QC.pdf has changed
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/peakdetection3_QC.pdf
b
Binary file test-data/peakdetection3_QC.pdf has changed
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/peakdetection4_QC.pdf
b
Binary file test-data/peakdetection4_QC.pdf has changed
b
diff -r 0892a051eb17 -r e754c2b545a9 test-data/testfile_squares.rdata
b
Binary file test-data/testfile_squares.rdata has changed