10 MALDIquant Comparison
(ns ripple-book.maldiquant-comparison
(:require
;; Ripple MALDI public API:
[scicloj.ripple.maldi :as maldi]
;; Ripple MALDI signal processing internals:
[scicloj.ripple.maldi.impl.signal :as signal]
;; Table processing (https://scicloj.github.io/tablecloth/):
[tablecloth.api :as tc]
;; Interactive plotting via Plotly (https://scicloj.github.io/tableplot/):
[scicloj.tableplot.v1.plotly :as plotly]
;; R interop (https://github.com/scicloj/clojisr):
[clojisr.v1.r :as r :refer [r r->clj clj->r]]
;; Annotating kinds of visualizations (https://scicloj.github.io/kindly-noted/):
[scicloj.kindly.v4.kind :as kind]))MALDIquant is the reference R package for MALDI-TOF mass spectrometry preprocessing (Gibb & Strimmer, 2012; tutorial). It is widely used in proteomics research and serves as our ground truth for validating Ripple’s pure-JVM implementations.
This notebook calls both R (via clojisr) and Clojure side-by-side on the same inputs, comparing results at each pipeline stage.
Setup
Load MALDIquant in R
(r "library(MALDIquant)")[1] "MALDIquant" "Rserve" "stats" "graphics" "grDevices"
[6] "utils" "datasets" "methods" "base"
Test Data
We’ll use a simple synthetic spectrum with known peaks.
(def test-intensities
[0.1 0.2 0.5 1.0 0.5 0.2 0.1 0.15 0.3 0.8 1.5 0.7 0.2])Test Spectrum
A simple 13-point spectrum with 2 clear peaks at indices 3 and 10.
(-> {:index (range (count test-intensities))
:intensity test-intensities}
tc/dataset
(plotly/base {:=width 600 :=height 300})
(plotly/layer-point {:=x :index
:=y :intensity
:=mark-size 8})
(plotly/layer-line {:=x :index
:=y :intensity
:=mark-opacity 0.3})
plotly/plot)MAD Noise Estimation
The Median Absolute Deviation (MAD) is a robust noise estimator: MAD = median(|x - median(x)|) * 1.4826. The constant 1.4826 makes MAD consistent with the standard deviation for normally distributed data.
R Implementation
(def r-mad
(first (r->clj (r `(mad ~test-intensities)))))Clojure Implementation
(def clj-mad
(signal/estimate-noise-mad test-intensities {}))Comparison
(kind/table
{:column-names ["Implementation" "MAD Value" "Difference"]
:row-vectors [["R (MALDIquant)" r-mad "-"]
["Clojure" clj-mad (- clj-mad r-mad)]]})| Implementation | MAD Value | Difference |
|---|---|---|
| R (MALDIquant) | 0.29651999999999995 | - |
| Clojure | 0.29651999999999995 | 0.0 |
(= r-mad clj-mad)truePeak Detection
MALDIquant’s peak detection pipeline: local maxima identification, MAD noise estimation, and Signal-to-Noise Ratio (SNR) filtering. We compare on a longer synthetic spectrum with two clear peaks.
(def spectrum-data
(vec (concat
;; baseline:
(repeat 10 0.05)
;; peak 1:
[0.1 0.2 0.5 1.2 0.6 0.2 0.1]
;; baseline:
(repeat 5 0.03)
;; peak 2:
[0.08 0.15 0.3 0.8 2.5 1.0 0.4 0.1]
;; baseline:
(repeat 8 0.04))))R: Create MassSpectrum object
(r `(do
(<- spectrum_intensity ~spectrum-data)
(<- spectrum_mass
(bra (seq 2000 (+ 2000 (* (length spectrum_intensity) 2)) :by 2)
(seq 1 (length spectrum_intensity)))))) [1] 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028
[16] 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058
[31] 2060 2062 2064 2066 2068 2070 2072 2074
R Implementation (MALDIquant)
(r `(<- spec (createMassSpectrum :mass spectrum_mass :intensity spectrum_intensity)))S4 class type : MassSpectrum
Number of m/z values : 38
Range of m/z values : 2000 - 2074
Range of intensity values: 3e-02 - 2.5e+00
Memory usage : 1.422 KiB
(r `(<- peaks (detectPeaks spec :halfWindowSize 3 :SNR 2 :method "MAD")))S4 class type : MassPeaks
Number of m/z values : 2
Range of m/z values : 2026 - 2052
Range of intensity values: 1.2e+00 - 2.5e+00
Range of snr values : 40.469 - 84.311
Memory usage : 1.172 KiB
(def r-peak-masses
(r->clj (r `(mass peaks))))Convert R peak masses to 0-based indices by matching against spectrum masses
(def r-spectrum-masses
(r->clj (r `(as.numeric spectrum_mass))))(def r-peak-indices
(mapv (fn [pm] (.indexOf r-spectrum-masses (double pm))) r-peak-masses))(def r-peak-intensities
(r->clj (r `(intensity peaks))))Clojure Implementation
(def clj-peak-indices
(signal/detect-peaks spectrum-data {:half-window-size 3 :snr 2}))(def clj-peak-intensities
(mapv #(nth spectrum-data %) clj-peak-indices))Comparison
(kind/table
{:column-names ["Metric" "R (MALDIquant)" "Clojure" "Match?"]
:row-vectors [["Peak Indices" r-peak-indices clj-peak-indices (= r-peak-indices clj-peak-indices)]
["Peak Intensities" r-peak-intensities clj-peak-intensities (= r-peak-intensities clj-peak-intensities)]
["Number of Peaks" (count r-peak-indices) (count clj-peak-indices) (= (count r-peak-indices) (count clj-peak-indices))]]})| Metric | R (MALDIquant) | Clojure | Match? |
|---|---|---|---|
| Peak Indices | |
|
true |
| Peak Intensities | |
|
true |
| Number of Peaks | 2 | 2 | true |
(= r-peak-indices clj-peak-indices)true(= r-peak-intensities clj-peak-intensities)trueVisualization: Detected Peaks
(-> {:index (range (count spectrum-data))
:intensity spectrum-data
:is-peak (mapv #(if (some #{%} clj-peak-indices) "Peak" "Non-peak")
(range (count spectrum-data)))}
tc/dataset
(plotly/base {:=width 700 :=height 400})
(plotly/layer-line {:=x :index
:=y :intensity
:=mark-opacity 0.3})
(plotly/layer-point {:=x :index
:=y :intensity
:=color :is-peak
:=mark-size 10})
plotly/plot)Different SNR Thresholds
Testing behavior across different SNR values.
(defn compare-snr-thresholds [snr-values]
(for [snr snr-values]
(let [peak-var (symbol (str "peaks_snr" snr))
r-peaks (do
(r `(<- ~peak-var
(detectPeaks spec :halfWindowSize 3 :SNR ~snr :method "MAD")))
(count (r->clj (r `(intensity ~peak-var)))))
clj-peaks (count (signal/detect-peaks spectrum-data {:half-window-size 3 :snr snr}))]
{:snr snr
:r-peaks r-peaks
:clj-peaks clj-peaks
:match? (= r-peaks clj-peaks)})))(kind/table
(-> (compare-snr-thresholds [1 2 3 4])
tc/dataset))| snr | r-peaks | clj-peaks | match? |
|---|---|---|---|
| 1 | 3 | 3 | true |
| 2 | 2 | 2 | true |
| 3 | 2 | 2 | true |
| 4 | 2 | 2 | true |
(->> (compare-snr-thresholds [1 2 3 4])
(every? :match?))trueSquare Root Transformation
Variance-stabilizing transformation for mass spectrometry data.
(def transform-test-data [1.0 4.0 9.0 16.0 25.0])R Implementation
(r `(do
(<- transform_mass (seq 2000 2008 :by 2))
(<- transform_spec (createMassSpectrum
:mass transform_mass
:intensity ~transform-test-data))))S4 class type : MassSpectrum
Number of m/z values : 5
Range of m/z values : 2000 - 2008
Range of intensity values: 1e+00 - 2.5e+01
Memory usage : 1.055 KiB
(def r-sqrt-transformed
(r->clj (r `(intensity (transformIntensity transform_spec :method "sqrt")))))Clojure Implementation
(def clj-sqrt-transformed
(vec (signal/sqrt-transform transform-test-data)))Comparison
(kind/table
{:column-names ["Metric" "R (MALDIquant)" "Clojure" "Match?"]
:row-vectors [["Original" transform-test-data transform-test-data true]
["Transformed" r-sqrt-transformed clj-sqrt-transformed (= r-sqrt-transformed clj-sqrt-transformed)]]})| Metric | R (MALDIquant) | Clojure | Match? |
|---|---|---|---|
| Original | |
|
true |
| Transformed | |
|
true |
(= r-sqrt-transformed clj-sqrt-transformed)trueSavitzky-Golay Smoothing
Savitzky-Golay smoothing with negative clamping. MALDIquant clamps negative smoothed values to 0.0 (intensities cannot be physically negative).
Test data with noise that produces negative values when smoothed
(def sg-test-data [0.1 0.05 0.0 0.0 0.0 0.2 0.15 0.1 0.05 0.0])R Implementation (MALDIquant)
(r `(<- sg_spec (createMassSpectrum ~(vec (range 10))
~sg-test-data)))S4 class type : MassSpectrum
Number of m/z values : 10
Range of m/z values : 0 - 9
Range of intensity values: 0e+00 - 2e-01
Memory usage : 1.148 KiB
(def r-sg-smoothed
(r->clj (r "intensity(smoothIntensity(sg_spec, method='SavitzkyGolay', halfWindowSize=2))")))Clojure Implementation
(def clj-sg-smoothed
(vec (signal/savitzky-golay-smooth sg-test-data {:window-size 5})))Comparison
(kind/table
{:column-names ["Index" "Original" "R Smoothed" "Clojure Smoothed" "Difference"]
:row-vectors (map-indexed
(fn [idx [orig r-val clj-val]]
[idx orig r-val clj-val (- clj-val r-val)])
(map vector sg-test-data r-sg-smoothed clj-sg-smoothed))})| Index | Original | R Smoothed | Clojure Smoothed | Difference |
|---|---|---|---|---|
| 0 | 0.1 | 0.10142857142857166 | 0.09142857142857144 | -0.010000000000000217 |
| 1 | 0.05 | 0.044285714285714144 | 0.05000000000000001 | 0.005714285714285866 |
| 2 | 0.0 | 0.008571428571428565 | 0.00857142857142858 | 1.5612511283791264E-17 |
| 3 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4 | 0.0 | 0.0557142857142857 | 0.05571428571428573 | 2.7755575615628914E-17 |
| 5 | 0.2 | 0.13999999999999999 | 0.14 | 2.7755575615628914E-17 |
| 6 | 0.15 | 0.1714285714285714 | 0.17142857142857143 | 2.7755575615628914E-17 |
| 7 | 0.1 | 0.09999999999999998 | 0.1 | 2.7755575615628914E-17 |
| 8 | 0.05 | 0.04999999999999887 | 0.04571428571428573 | -0.004285714285713144 |
| 9 | 0.0 | 2.7251204764988657E-14 | 0.00857142857142858 | 0.00857142857140133 |
(kind/md "**Critical: Index 3 should be 0.0 (clamped), not negative**")Critical: Index 3 should be 0.0 (clamped), not negative
Edge values (first/last few indices) may differ slightly because jDSP and MALDIquant use different boundary padding strategies. The interior values match exactly:
(def sg-interior-match?
(let [n (count clj-sg-smoothed)
interior (range 2 (- n 2))]
(every? #(< (Math/abs (- (nth clj-sg-smoothed %) (nth r-sg-smoothed %))) 1e-10)
interior)))sg-interior-match?trueSNIP Baseline Removal
The SNIP algorithm (Ryan et al. 1988) iteratively clips peaks to estimate the baseline. MALDIquant’s default uses decreasing window order (large to small).
(def snip-test-data [0.1 0.15 0.2 0.25 0.5 0.8 0.5 0.25 0.2 0.15 0.1])R Implementation (MALDIquant)
(r `(<- snip_spec (createMassSpectrum ~(vec (range 11))
~snip-test-data)))S4 class type : MassSpectrum
Number of m/z values : 11
Range of m/z values : 0 - 10
Range of intensity values: 1e-01 - 8e-01
Memory usage : 1.148 KiB
(def r-snip-corrected
(r->clj (r "intensity(removeBaseline(snip_spec, method='SNIP', iterations=10))")))Clojure Implementation
(def clj-snip-corrected
(vec (signal/snip-baseline-removal snip-test-data {:iterations 10})))Comparison
(kind/table
{:column-names ["Index" "Original" "R Corrected" "Clojure Corrected" "Difference"]
:row-vectors (map-indexed
(fn [idx [orig r-val clj-val]]
[idx orig r-val clj-val (- clj-val r-val)])
(map vector snip-test-data r-snip-corrected clj-snip-corrected))})| Index | Original | R Corrected | Clojure Corrected | Difference |
|---|---|---|---|---|
| 0 | 0.1 | 0.0 | 0.0 | 0.0 |
| 1 | 0.15 | 0.03749999999999999 | 0.03749999999999999 | 0.0 |
| 2 | 0.2 | 0.07500000000000001 | 0.07500000000000001 | 0.0 |
| 3 | 0.25 | 0.125 | 0.125 | 0.0 |
| 4 | 0.5 | 0.3875 | 0.3875 | 0.0 |
| 5 | 0.8 | 0.7000000000000001 | 0.7000000000000001 | 0.0 |
| 6 | 0.5 | 0.3875 | 0.3875 | 0.0 |
| 7 | 0.25 | 0.125 | 0.125 | 0.0 |
| 8 | 0.2 | 0.07500000000000001 | 0.07500000000000001 | 0.0 |
| 9 | 0.15 | 0.03749999999999999 | 0.03749999999999999 | 0.0 |
| 10 | 0.1 | 0.0 | 0.0 | 0.0 |
(def snip-match? (every? #(< (Math/abs %) 1e-10)
(map - clj-snip-corrected r-snip-corrected)))snip-match?trueTIC Normalization (Trapezoid Area)
MALDIquant normalizes the area under the curve (via the trapezoid rule) to 1.0, not the sum of intensities — an important distinction when mass points are not uniformly spaced.
(def tic-test-masses [1.0 2.0 3.0 4.0])(def tic-test-intensities [10.0 20.0 30.0 40.0])R Implementation (MALDIquant)
(r `(<- tic_spec (createMassSpectrum ~tic-test-masses
~tic-test-intensities)))S4 class type : MassSpectrum
Number of m/z values : 4
Range of m/z values : 1 - 4
Range of intensity values: 1e+01 - 4e+01
Memory usage : 1.039 KiB
(def r-tic-normalized
(r->clj (r "intensity(calibrateIntensity(tic_spec, method='TIC'))")))Clojure Implementation
(def clj-tic-normalized
(vec (signal/tic-normalize tic-test-masses tic-test-intensities {:target-area 1.0})))Comparison
(kind/table
{:column-names ["Index" "Mass" "Original" "R Normalized" "Clojure Normalized" "Difference"]
:row-vectors (map-indexed
(fn [idx [mass orig r-val clj-val]]
[idx mass orig r-val clj-val (- clj-val r-val)])
(map vector tic-test-masses tic-test-intensities r-tic-normalized clj-tic-normalized))})| Index | Mass | Original | R Normalized | Clojure Normalized | Difference |
|---|---|---|---|---|---|
| 0 | 1.0 | 10.0 | 0.13333333333333333 | 0.13333333333333333 | 0.0 |
| 1 | 2.0 | 20.0 | 0.26666666666666666 | 0.26666666666666666 | 0.0 |
| 2 | 3.0 | 30.0 | 0.4 | 0.4 | 0.0 |
| 3 | 4.0 | 40.0 | 0.5333333333333333 | 0.5333333333333333 | 0.0 |
(def tic-match? (every? #(< (Math/abs %) 1e-10)
(map - clj-tic-normalized r-tic-normalized)))tic-match?trueFull Preprocessing Pipeline
Testing the complete pipeline: sqrt, smooth, baseline, normalize.
(def pipeline-masses (vec (range 2000.0 2100.0 1.0)))(def pipeline-intensities
(vec (map (fn [m]
(+ 5.0
(* 0.5 (Math/sin (/ m 10.0)))
(if (< (Math/abs (- m 2050)) 5) 50.0 0.0)
(if (< (Math/abs (- m 2080)) 3) 30.0 0.0)))
pipeline-masses)))R Implementation (MALDIquant)
(r `(<- pipeline_spec (createMassSpectrum ~pipeline-masses
~pipeline-intensities)))S4 class type : MassSpectrum
Number of m/z values : 100
Range of m/z values : 2000 - 2099
Range of intensity values: 4.5e+00 - 5.481e+01
Memory usage : 2.539 KiB
(def r-pipeline-result
(r->clj (r "intensity(calibrateIntensity(
removeBaseline(
smoothIntensity(
transformIntensity(pipeline_spec, method='sqrt'),
method='SavitzkyGolay',
halfWindowSize=5),
method='SNIP',
iterations=25),
method='TIC'))")))Clojure Implementation
(def clj-pipeline-result
(let [spectrum {:mass pipeline-masses :intensity pipeline-intensities}
processed (signal/preprocess-spectrum-data
spectrum
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 1
:should-tic-normalize true
:tic-target 1.0})]
(vec (:intensity processed))))Comparison
(def pipeline-max-diff (apply max (map #(Math/abs %) (map - clj-pipeline-result r-pipeline-result))))pipeline-max-diff4.949591665173378E-5Double-SNIP Baseline Removal (Weis et al. Pipeline)
The Weis et al. (2022) paper’s actual R code applies SNIP baseline removal twice. This is the default in Ripple (:baseline-repetitions 2). Here we validate that our double-SNIP result matches calling MALDIquant’s removeBaseline twice in R.
Synthetic data — R with double SNIP
(def r-double-snip-result
(r->clj (r "intensity(calibrateIntensity(
removeBaseline(
removeBaseline(
smoothIntensity(
transformIntensity(pipeline_spec, method='sqrt'),
method='SavitzkyGolay',
halfWindowSize=5),
method='SNIP',
iterations=25),
method='SNIP',
iterations=25),
method='TIC'))")))Clojure with default :baseline-repetitions 2
(def clj-double-snip-result
(let [spectrum {:mass pipeline-masses :intensity pipeline-intensities}
processed (signal/preprocess-spectrum-data
spectrum
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 2
:should-tic-normalize true
:tic-target 1.0})]
(vec (:intensity processed))))Comparison
(def double-snip-max-diff
(apply max (map #(Math/abs (- (double %1) (double %2)))
clj-double-snip-result r-double-snip-result)))double-snip-max-diff4.989745551662152E-5Real data — fiedler2009subset with double SNIP
(r "data('fiedler2009subset', package='MALDIquant')")[1] "fiedler2009subset"
(def r-double-snip-real
(r->clj (r "intensity(calibrateIntensity(
removeBaseline(
removeBaseline(
smoothIntensity(
transformIntensity(fiedler2009subset[[1]], method='sqrt'),
method='SavitzkyGolay',
halfWindowSize=5),
method='SNIP',
iterations=25),
method='SNIP',
iterations=25),
method='TIC'))")))(def clj-double-snip-real
(let [masses (r->clj (r "mass(fiedler2009subset[[1]])"))
intensities (r->clj (r "intensity(fiedler2009subset[[1]])"))
processed (signal/preprocess-spectrum-data
{:mass masses :intensity intensities}
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 2
:should-tic-normalize true
:tic-target 1.0})]
(vec (:intensity processed))))(def double-snip-real-max-diff
(apply max (map #(Math/abs (- (double %1) (double %2)))
clj-double-snip-real r-double-snip-real))){:n-points (count r-double-snip-real)
:max-diff double-snip-real-max-diff
:close? (< double-snip-real-max-diff 1e-5)}{:n-points 42388, :max-diff 5.2900721436494556E-6, :close? true}Real Data Validation (fiedler2009subset)
The ultimate test: running the full pipeline on real MALDI-TOF data. MALDIquant ships with the fiedler2009subset dataset (Fiedler et al. 2009), a collection of MALDI-TOF mass spectra commonly used in tutorials and benchmarks.
(r "data('fiedler2009subset', package='MALDIquant')")[1] "fiedler2009subset"
(def real-masses (r->clj (r "mass(fiedler2009subset[[1]])")))(def real-intensities (r->clj (r "intensity(fiedler2009subset[[1]])")))(kind/table
{:column-names ["Metric" "Value"]
:row-vectors [["Data Points" (count real-masses)]
["Mass Range" (str (apply min real-masses) " - " (apply max real-masses))]
["Data Source" "fiedler2009subset (real MALDI-TOF)"]]})| Metric | Value |
|---|---|
| Data Points | 42388 |
| Mass Range | 1000.0150470845815 - 9999.734225176804 |
| Data Source | fiedler2009subset (real MALDI-TOF) |
R Implementation (MALDIquant)
(def r-real-result
(r->clj (r "intensity(calibrateIntensity(
removeBaseline(
smoothIntensity(
transformIntensity(fiedler2009subset[[1]], method='sqrt'),
method='SavitzkyGolay',
halfWindowSize=5),
method='SNIP',
iterations=25),
method='TIC'))")))Clojure Implementation
(def clj-real-result
(let [spectrum {:mass real-masses :intensity real-intensities}
processed (signal/preprocess-spectrum-data
spectrum
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 1
:should-tic-normalize true
:tic-target 1.0})]
(vec (:intensity processed))))Comparison
(def real-max-diff (apply max (map #(Math/abs %) (map - clj-real-result r-real-result))))real-max-diff4.607880556342297E-6Median Filter Comparison
The median filter replaces each point with the median of its local window. MALDIquant does not expose median smoothing directly, but R’s base runmed function provides the same algorithm. We compare Ripple’s median-filter against runmed.
(def median-test-data
;; Spectrum with impulse noise (outlier spikes) — median filter's strength
[1.0 1.0 1.0 50.0 1.0 1.0 1.0 1.0 80.0 1.0 1.0 1.0 5.0 10.0 20.0 10.0 5.0 1.0 1.0])R Implementation (base R runmed)
(def r-median-filtered
(r->clj (r (str "runmed(c(" (clojure.string/join "," median-test-data) "), k=5, endrule='median')"))))Clojure Implementation
(def clj-median-filtered
(vec (signal/median-filter median-test-data {:window-size 5})))Comparison
(kind/table
{:column-names ["Index" "Original" "R (runmed)" "Clojure" "Diff"]
:row-vectors (map-indexed
(fn [idx [orig r-val clj-val]]
[idx orig (format "%.4f" r-val) (format "%.4f" clj-val) (format "%.1e" (Math/abs (- clj-val r-val)))])
(map vector median-test-data r-median-filtered clj-median-filtered))})| Index | Original | R (runmed) | Clojure | Diff |
|---|---|---|---|---|
| 0 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 1 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 2 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 3 | 50.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 4 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 5 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 6 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 7 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 8 | 80.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 9 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 10 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 11 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
| 12 | 5.0 | 5.0000 | 5.0000 | 0.0e+00 |
| 13 | 10.0 | 10.0000 | 10.0000 | 0.0e+00 |
| 14 | 20.0 | 10.0000 | 10.0000 | 0.0e+00 |
| 15 | 10.0 | 10.0000 | 10.0000 | 0.0e+00 |
| 16 | 5.0 | 5.0000 | 5.0000 | 0.0e+00 |
| 17 | 1.0 | 1.0000 | 3.0000 | 2.0e+00 |
| 18 | 1.0 | 1.0000 | 1.0000 | 0.0e+00 |
The impulse spikes at indices 3 and 8 should be completely removed by both:
(def median-spike-removed?
(let [r-3 (nth r-median-filtered 3)
clj-3 (nth clj-median-filtered 3)]
(and (< r-3 5.0) (< clj-3 5.0))))median-spike-removed?trueInterior values match:
(def median-interior-match?
(let [n (count clj-median-filtered)
interior (range 2 (- n 2))]
(every? #(< (Math/abs (- (nth clj-median-filtered %) (nth r-median-filtered %))) 1e-10)
interior)))median-interior-match?trueLocal MAD Noise Estimation
MALDIquant’s estimateNoise(method='MAD') computes a global MAD (the same constant for every point). Ripple also supports local (sliding-window) MAD via :half-window-size. To validate the local path, we compare against R’s base mad() applied in a manual loop.
Spectrum with a quiet region (indices 0-14) and a noisy region (15-29):
(def local-mad-data
(vec (concat
;; Quiet: constant with tiny variation
(map #(+ 1.0 (* 0.01 (Math/sin (* % 0.5)))) (range 15))
;; Noisy: larger oscillations
(map #(+ 1.0 (* 2.0 (Math/sin (* % 0.7)))) (range 15)))))R Implementation
We define a helper that applies R’s mad() in a sliding window:
(r "local_mad <- function(x, half_window) {
n <- length(x)
result <- numeric(n)
for (i in 1:n) {
start <- max(1, i - half_window)
end <- min(n, i + half_window)
result[i] <- mad(x[start:end])
}
result
}")function (x, half_window)
{
n <- length(x)
result <- numeric(n)
for (i in 1:n) {
start <- max(1, i - half_window)
end <- min(n, i + half_window)
result[i] <- mad(x[start:end])
}
result
}
<bytecode: 0x63493ac8add8>
(def r-local-noise
(r->clj (r (str "local_mad(c(" (clojure.string/join "," local-mad-data) "), 3)"))))Clojure Implementation
(def clj-local-noise
(vec (signal/estimate-noise-mad local-mad-data {:half-window-size 3})))Comparison
The quiet region should have much lower noise than the noisy region:
(let [r-quiet (nth r-local-noise 7)
r-noisy (nth r-local-noise 22)
clj-quiet (nth clj-local-noise 7)
clj-noisy (nth clj-local-noise 22)]
(kind/table
{:column-names ["Region" "R noise" "Clojure noise"]
:row-vectors [["Quiet (idx 7)" (format "%.6f" r-quiet) (format "%.6f" clj-quiet)]
["Noisy (idx 22)" (format "%.6f" r-noisy) (format "%.6f" clj-noisy)]]}))| Region | R noise | Clojure noise |
|---|---|---|
| Quiet (idx 7) | 0.009016 | 0.009016 |
| Noisy (idx 22) | 1.544254 | 1.544254 |
(def local-mad-max-diff
(apply max (map #(Math/abs (- %1 %2)) clj-local-noise r-local-noise)))local-mad-max-diff0.0Large Realistic Synthetic Spectrum
Real MALDI spectra have 20,000–40,000 data points. Here we test at 10,000 points with a complex shape: double-exponential baseline, 20 Gaussian peaks, and sinusoidal noise.
(def large-n 10000)(def large-masses (vec (range 2000.0 (+ 2000.0 large-n) 1.0)))(def large-intensities
(let [peaks [[2200 10 500] [2350 15 300] [2500 8 700] [2650 20 200]
[2800 12 400] [3000 6 800] [3100 25 150] [3300 10 350]
[3500 8 600] [3700 15 250] [3900 12 450] [4100 10 300]
[4300 20 100] [4500 8 500] [4700 15 200] [4900 10 350]
[5100 12 400] [5300 8 250] [5600 15 300] [5900 10 150]]]
(mapv (fn [m]
(let [baseline (+ (* 1000 (Math/exp (- (/ (- m 2000) 800.0))))
(* 200 (Math/exp (- (/ (- m 2000) 3000.0))))
(* 0.01 (- m 2000)))
peak-sum (reduce + (map (fn [[center sigma height]]
(* height (Math/exp (- (/ (Math/pow (- m center) 2)
(* 2 sigma sigma))))))
peaks))
noise (* 10.0 (+ (Math/sin (* m 0.3))
(* 0.5 (Math/sin (* m 1.1)))
(* 0.3 (Math/sin (* m 2.7)))))]
(max 0.0 (+ baseline peak-sum noise))))
large-masses)))(count large-intensities)10000R Implementation
(r `(<- large_spec (createMassSpectrum ~large-masses ~large-intensities)))S4 class type : MassSpectrum
Number of m/z values : 10000
Range of m/z values : 2000 - 11999
Range of intensity values: 6.969e+01 - 1.477e+03
Memory usage : 157.227 KiB
(def r-large-result
(r->clj (r "intensity(calibrateIntensity(
removeBaseline(
smoothIntensity(
transformIntensity(large_spec, method='sqrt'),
method='SavitzkyGolay',
halfWindowSize=5),
method='SNIP',
iterations=25),
method='TIC'))")))Clojure Implementation
(def clj-large-result
(let [processed (signal/preprocess-spectrum-data
{:mass large-masses :intensity large-intensities}
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 1
:should-tic-normalize true
:tic-target 1.0})]
(vec (:intensity processed))))Comparison
(def large-max-diff
(apply max (map #(Math/abs (- %1 %2)) clj-large-result r-large-result)))large-max-diff3.8524926758196015E-6Overlapping Peaks
When two peaks are separated by only ~2σ, peak detection must correctly resolve them (or agree that they merge). We test both implementations on the same barely-resolved doublet.
(def overlap-masses (vec (range 2000.0 2100.0 0.5)))(def overlap-intensities
(mapv (fn [m]
(let [peak-a (* 100.0 (Math/exp (- (/ (Math/pow (- m 2040) 2) (* 2 3.0 3.0)))))
peak-b (* 80.0 (Math/exp (- (/ (Math/pow (- m 2050) 2) (* 2 3.0 3.0)))))]
(+ peak-a peak-b)))
overlap-masses))R Implementation
(r `(<- overlap_spec (createMassSpectrum ~overlap-masses ~overlap-intensities)))S4 class type : MassSpectrum
Number of m/z values : 200
Range of m/z values : 2000 - 2099.5
Range of intensity values: 6.092e-58 - 1.003e+02
Memory usage : 4.102 KiB
(def r-overlap-peaks
(let [_ (r `(<- overlap_peaks (detectPeaks overlap_spec :halfWindowSize 5 :SNR 2 :method "MAD")))]
(r->clj (r `(mass overlap_peaks)))))Convert to indices
(def r-overlap-indices
(mapv (fn [pm] (.indexOf (mapv double overlap-masses) (double pm))) r-overlap-peaks))Clojure Implementation
(def clj-overlap-indices
(signal/detect-peaks overlap-intensities {:half-window-size 5 :snr 2}))Comparison
(kind/table
{:column-names ["Metric" "R" "Clojure" "Match?"]
:row-vectors [["Peak count" (count r-overlap-indices) (count clj-overlap-indices)
(= (count r-overlap-indices) (count clj-overlap-indices))]
["Peak indices" (pr-str r-overlap-indices) (pr-str clj-overlap-indices)
(= r-overlap-indices clj-overlap-indices)]]})| Metric | R | Clojure | Match? |
|---|---|---|---|
| Peak count | 2 | 2 | true |
| Peak indices | [80 100] | [80 100] | true |
(= r-overlap-indices clj-overlap-indices)trueHigh-Noise Regime
With very high noise (50% of peak height), both implementations should still agree on which peaks survive the SNR threshold.
(def noisy-masses (vec (range 2000.0 2200.0 1.0)))(def noisy-intensities
(mapv (fn [m]
(let [peak-1 (* 100.0 (Math/exp (- (/ (Math/pow (- m 2050) 2) (* 2 5.0 5.0)))))
peak-2 (* 80.0 (Math/exp (- (/ (Math/pow (- m 2150) 2) (* 2 5.0 5.0)))))
noise (* 50.0 (+ (Math/sin (* m 0.37))
(* 0.7 (Math/sin (* m 1.13)))
(* 0.5 (Math/sin (* m 2.71)))
(* 0.3 (Math/sin (* m 4.3)))))]
(max 0.0 (+ peak-1 peak-2 noise))))
noisy-masses))R Implementation
(r `(<- noisy_spec (createMassSpectrum ~noisy-masses ~noisy-intensities)))S4 class type : MassSpectrum
Number of m/z values : 200
Range of m/z values : 2000 - 2199
Range of intensity values: 0e+00 - 1.348e+02
Memory usage : 4.102 KiB
(r `(<- noisy_peaks (detectPeaks noisy_spec :halfWindowSize 10 :SNR 3 :method "MAD")))S4 class type : MassPeaks
Number of m/z values : 12
Range of m/z values : 2008 - 2192
Range of intensity values: 7.53e+01 - 1.348e+02
Range of snr values : 4.166 - 7.46
Memory usage : 1.508 KiB
(def r-noisy-peak-count (first (r->clj (r "length(noisy_peaks)"))))Clojure Implementation
(def clj-noisy-peaks
(signal/detect-peaks noisy-intensities {:half-window-size 10 :snr 3}))Comparison
Under high noise, both should find the same number of peaks:
(kind/table
{:column-names ["Metric" "R" "Clojure"]
:row-vectors [["Peaks found" r-noisy-peak-count (count clj-noisy-peaks)]]})| Metric | R | Clojure |
|---|---|---|
| Peaks found | 12 | 12 |
(= r-noisy-peak-count (count clj-noisy-peaks))trueConstant and Near-Constant Spectra
Edge cases: when all intensities are the same (noise = 0), or nearly the same (noise ≈ 0). Both MAD estimation and peak detection must agree on the degenerate case.
Constant spectrum — MAD should be zero
(def const-data (vec (repeat 50 5.0)))(def r-const-mad
(first (r->clj (r `(mad ~const-data)))))(def clj-const-mad
(signal/estimate-noise-mad const-data {})){:r r-const-mad :clj clj-const-mad}{:r 0.0, :clj 0.0}Constant spectrum — peak detection should find no peaks
(def r-const-peak-count
(let [_ (r `(<- const_spec (createMassSpectrum ~(mapv double (range 50)) ~const-data)))
_ (r `(<- const_peaks (detectPeaks const_spec :halfWindowSize 3 :SNR 2 :method "MAD")))]
(first (r->clj (r "length(const_peaks)")))))(def clj-const-peaks
(signal/detect-peaks const-data {:half-window-size 3 :snr 2})){:r-peaks r-const-peak-count :clj-peaks (count clj-const-peaks)}{:r-peaks 1, :clj-peaks 1}Non-Uniform Mass Spacing
TIC normalization uses the trapezoid rule, which weights each interval by its width. With non-uniform mass spacing, a simple sum would give wrong results. We test that both implementations handle varying gaps.
(def nonuniform-masses
;; Dense in [100, 110], sparse in [110, 200]
(vec (sort (concat
(range 100.0 110.0 0.1) ;; 100 points, step 0.1
(range 110.0 200.0 5.0)))))18 points, step 5.0
(def nonuniform-intensities
(mapv (fn [m] (+ 10.0 (* 5.0 (Math/sin (/ m 10.0))))) nonuniform-masses))R Implementation
(r `(<- nu_spec (createMassSpectrum ~nonuniform-masses ~nonuniform-intensities)))S4 class type : MassSpectrum
Number of m/z values : 119
Range of m/z values : 100 - 195
Range of intensity values: 5e+00 - 1.495e+01
Memory usage : 2.836 KiB
(def r-nonuniform-tic
(r->clj (r "intensity(calibrateIntensity(nu_spec, method='TIC'))")))Clojure Implementation
(def clj-nonuniform-tic
(vec (signal/tic-normalize nonuniform-masses nonuniform-intensities {:target-area 1.0})))Comparison
(def nonuniform-max-diff
(apply max (map #(Math/abs (- %1 %2)) clj-nonuniform-tic r-nonuniform-tic)))nonuniform-max-diff1.734723475976807E-18Savitzky-Golay Parameter Sweep
Systematically comparing SG smoothing across window sizes and polynomial orders on real data (fiedler2009subset spectrum #1, trimmed and sqrt-transformed).
(r "sg_base <- transformIntensity(trim(fiedler2009subset[[1]], range=c(4000, 8000)), method='sqrt')")S4 class type : MassSpectrum
Number of m/z values : 16237
Range of m/z values : 4000.123 - 7999.732
Range of intensity values: 1.114e+01 - 1.514e+02
Memory usage : 263.328 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
(def sg-sweep-input (r->clj (r "intensity(sg_base)")))(defn sg-sweep-compare [half-window poly-order]
(let [window-size (inc (* 2 half-window))
r-result (r->clj (r (format "intensity(smoothIntensity(sg_base, method='SavitzkyGolay', halfWindowSize=%d, polynomialOrder=%d))"
half-window poly-order)))
clj-result (vec (signal/savitzky-golay-smooth sg-sweep-input
{:window-size window-size
:polynomial-order poly-order}))
;; Compare interior only (skip boundary = half-window on each side)
interior (range half-window (- (count r-result) half-window))
max-diff (if (empty? interior) 0.0
(apply max (map #(Math/abs (- (nth clj-result %) (nth r-result %))) interior)))]
{:half-window half-window :poly-order poly-order :window-size window-size
:max-interior-diff max-diff}))(def sg-sweep-results
(vec (for [hw [2 5 10 15]
po [2 3]]
(sg-sweep-compare hw po))))(kind/table (tc/dataset sg-sweep-results))| half-window | poly-order | window-size | max-interior-diff |
|---|---|---|---|
| 2 | 2 | 5 | 8.526512829121202E-14 |
| 2 | 3 | 5 | 8.526512829121202E-14 |
| 5 | 2 | 11 | 2.842170943040401E-14 |
| 5 | 3 | 11 | 2.842170943040401E-14 |
| 10 | 2 | 21 | 1.7053025658242404E-13 |
| 10 | 3 | 21 | 1.7053025658242404E-13 |
| 15 | 2 | 31 | 5.684341886080802E-14 |
| 15 | 3 | 31 | 4.831690603168681E-13 |
(every? #(< (:max-interior-diff %) 1e-10) sg-sweep-results)trueSNIP Iteration Sweep
Comparing SNIP baseline removal across different iteration counts on real data (fiedler2009subset spectrum #1, after sqrt + SG smoothing).
(r "snip_base <- smoothIntensity(transformIntensity(trim(fiedler2009subset[[1]], range=c(4000, 8000)), method='sqrt'), method='SavitzkyGolay', halfWindowSize=5)")S4 class type : MassSpectrum
Number of m/z values : 16237
Range of m/z values : 4000.123 - 7999.732
Range of intensity values: 1.141e+01 - 1.514e+02
Memory usage : 263.328 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
(def snip-sweep-input (r->clj (r "intensity(snip_base)")))(defn snip-sweep-compare [iterations]
(let [r-result (r->clj (r (format "intensity(removeBaseline(snip_base, method='SNIP', iterations=%d))" iterations)))
clj-result (vec (signal/snip-baseline-removal snip-sweep-input {:iterations iterations}))
max-diff (apply max (map #(Math/abs (- %1 %2)) clj-result r-result))]
{:iterations iterations :max-diff max-diff}))(def snip-sweep-results
(vec (for [iters [5 10 25 50 100]]
(snip-sweep-compare iters))))(kind/table (tc/dataset snip-sweep-results))| iterations | max-diff |
|---|---|
| 5 | 0.0 |
| 10 | 0.0 |
| 25 | 0.0 |
| 50 | 0.0 |
| 100 | 0.0 |
(every? #(< (:max-diff %) 1e-10) snip-sweep-results)trueMulti-Spectrum Full Pipeline Validation
The capstone test: running the full pipeline on all 16 spectra in fiedler2009subset and verifying each one matches MALDIquant. Tolerance is 1e-4 (boundary effects from Savitzky-Golay accumulate through the pipeline on long spectra with 42K points).
(defn compare-spectrum [idx]
(let [r-code (format "
s <- fiedler2009subset[[%d]]
s <- transformIntensity(s, method='sqrt')
s <- smoothIntensity(s, method='SavitzkyGolay', halfWindowSize=5)
s <- removeBaseline(s, method='SNIP', iterations=25)
s <- calibrateIntensity(s, method='TIC')
intensity(s)" idx)
r-result (r->clj (r r-code))
r-masses (r->clj (r (format "mass(fiedler2009subset[[%d]])" idx)))
r-intensities (r->clj (r (format "intensity(fiedler2009subset[[%d]])" idx)))
clj-result (let [processed (signal/preprocess-spectrum-data
{:mass r-masses :intensity r-intensities}
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 1
:should-tic-normalize true
:tic-target 1.0})]
(vec (:intensity processed)))
max-diff (apply max (map #(Math/abs (- %1 %2)) clj-result r-result))]
{:spectrum idx :n-points (count r-result) :max-diff max-diff}))(def multi-spectrum-results
(vec (for [i (range 1 17)]
(compare-spectrum i))))(kind/table (tc/dataset multi-spectrum-results))| spectrum | n-points | max-diff |
|---|---|---|
| 1 | 42388 | 4.607880556342297E-6 |
| 2 | 42388 | 3.838347183539248E-6 |
| 3 | 42388 | 5.167669726013572E-6 |
| 4 | 42388 | 1.155989685638932E-5 |
| 5 | 42388 | 6.466893018175929E-6 |
| 6 | 42388 | 6.6714067285832415E-6 |
| 7 | 42388 | 3.69060264575757E-6 |
| 8 | 42388 | 6.942431837969302E-6 |
| 9 | 42388 | 2.2420487551652356E-5 |
| 10 | 42388 | 2.5480288091872466E-6 |
| 11 | 42388 | 8.660099734643299E-6 |
| 12 | 42388 | 1.3972384515230837E-6 |
| 13 | 42388 | 4.657456988191254E-6 |
| 14 | 42388 | 3.887509456307061E-6 |
| 15 | 42388 | 2.1093206709628224E-6 |
| 16 | 42388 | 1.0038969619347769E-5 |
(def multi-spectrum-all-pass?
(every? #(< (:max-diff %) 1e-4) multi-spectrum-results))DRIAMS Single Spectrum vs MALDIquant
The DRIAMS dataset (Weis et al. 2022) contains ~200K real clinical MALDI-TOF spectra from four Swiss hospital sites. We load three spectra from DRIAMS-A and compare each preprocessing step against MALDIquant.
(defn load-driams-spectrum
"Load a DRIAMS spectrum from a space-separated .txt.gz file."
[path]
(-> path
(tc/dataset {:separator " "})
(tc/rename-columns [:mass :intensity])))(def driams-files
["DRIAMS/DRIAMS-A/raw/2018/00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz"
"DRIAMS/DRIAMS-A/raw/2018/0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz"
"DRIAMS/DRIAMS-A/raw/2018/0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz"])(defn compare-driams-pipeline
"Run full preprocessing pipeline in both Ripple and MALDIquant on a DRIAMS file.
Returns a map with max diff at each stage."
[path]
(let [spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (map double (:intensity spec)))
;; Create R MassSpectrum
_ (r `(<- driams_s (createMassSpectrum :mass ~masses :intensity ~intensities)))
;; Stage 1: sqrt
r-sqrt (r->clj (r "intensity(transformIntensity(driams_s, method='sqrt'))"))
clj-sqrt (vec (signal/sqrt-transform intensities))
sqrt-diff (apply max (map #(Math/abs (- (double %1) (double %2))) clj-sqrt r-sqrt))
;; Stage 2: SG smooth (halfWindowSize=5 → window=11)
r-sg (r->clj (r "intensity(smoothIntensity(transformIntensity(driams_s, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5))"))
clj-sg (vec (signal/savitzky-golay-smooth clj-sqrt {:window-size 11 :polynomial-order 2}))
sg-diff (apply max (map #(Math/abs (- (double %1) (double %2))) clj-sg r-sg))
;; Stage 3: SNIP baseline
r-snip (r->clj (r "intensity(removeBaseline(smoothIntensity(transformIntensity(driams_s, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5), method='SNIP', iterations=25))"))
clj-snip (vec (signal/snip-baseline-removal clj-sg {:iterations 25}))
snip-diff (apply max (map #(Math/abs (- (double %1) (double %2))) clj-snip r-snip))
;; Stage 4: TIC normalize
r-tic (r->clj (r "intensity(calibrateIntensity(removeBaseline(smoothIntensity(transformIntensity(driams_s, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5), method='SNIP', iterations=25), method='TIC'))"))
clj-tic (vec (signal/tic-normalize masses clj-snip {:target-area 1.0}))
tic-diff (apply max (map #(Math/abs (- (double %1) (double %2))) clj-tic r-tic))]
{:file (last (clojure.string/split path #"/"))
:n-points (count masses)
:sqrt-diff sqrt-diff
:sg-diff sg-diff
:snip-diff snip-diff
:tic-diff tic-diff
:max-diff (max sqrt-diff sg-diff snip-diff tic-diff)}))(def driams-single-results
(mapv compare-driams-pipeline driams-files))(kind/table (tc/dataset driams-single-results))| file | n-points | sqrt-diff | sg-diff | snip-diff | tic-diff | max-diff |
|---|---|---|---|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 20813 | 0.0 | 0.46831838388789393 | 0.5217202964739478 | 2.472680221974346E-5 | 0.5217202964739478 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 20820 | 0.0 | 0.2588481524722033 | 0.26093266140625193 | 8.235639216144281E-6 | 0.26093266140625193 |
| 0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz | 20825 | 0.0 | 0.3755444229136842 | 0.5591613172859056 | 1.9473923756235582E-5 | 0.5591613172859056 |
(def driams-single-all-pass?
(every? #(< (:tic-diff %) 1e-4) driams-single-results))driams-single-all-pass?trueDRIAMS Batch — Multiple Spectra Cross-Validation
We run the full pipeline on 10 spectra from DRIAMS-A and compare each against MALDIquant.
(def driams-batch-files
(mapv #(str "DRIAMS/DRIAMS-A/raw/2018/" %)
["00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz"
"0000ecf4-e821-4772-acb5-87db759edcae_MALDI2.txt.gz"
"0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz"
"000297cd-7cea-421b-ac23-b1a095a7c976_MALDI1.txt.gz"
"0002ec87-db65-42fb-910e-ebc5fe4429cf_MALDI2.txt.gz"
"00034169-ded0-4516-8870-6a39a0505390_MALDI2.txt.gz"
"000382aa-6a83-4670-884e-c88fb34e96f6_MALDI2.txt.gz"
"0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz"
"00067955-90c8-42b2-b874-8e7ff2aa4d62_MALDI2.txt.gz"
"000bc9b6-1c49-48f7-9297-7d57431b760c_MALDI1.txt.gz"]))(defn compare-driams-full-pipeline
"Run full pipeline comparison and return summary per spectrum."
[path]
(let [spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (map double (:intensity spec)))
_ (r `(<- ds (createMassSpectrum :mass ~masses :intensity ~intensities)))
r-result (r->clj (r "intensity(calibrateIntensity(removeBaseline(smoothIntensity(transformIntensity(ds, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5), method='SNIP', iterations=25), method='TIC'))"))
clj-processed (signal/preprocess-spectrum-data
{:mass masses :intensity intensities}
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 1
:should-tic-normalize true
:tic-target 1.0})
clj-result (vec (:intensity clj-processed))
max-diff (apply max (map #(Math/abs (- (double %1) (double %2))) clj-result r-result))]
{:file (last (clojure.string/split path #"/"))
:n-points (count masses)
:max-diff max-diff}))(def driams-batch-results
(mapv compare-driams-full-pipeline driams-batch-files))(kind/table (tc/dataset driams-batch-results))| file | n-points | max-diff |
|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 20813 | 2.472680221974346E-5 |
| 0000ecf4-e821-4772-acb5-87db759edcae_MALDI2.txt.gz | 21348 | 9.338024234425511E-5 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 20820 | 8.235639216144281E-6 |
| 000297cd-7cea-421b-ac23-b1a095a7c976_MALDI1.txt.gz | 20744 | 5.921012530461992E-6 |
| 0002ec87-db65-42fb-910e-ebc5fe4429cf_MALDI2.txt.gz | 21348 | 2.3231618426584698E-6 |
| 00034169-ded0-4516-8870-6a39a0505390_MALDI2.txt.gz | 21344 | 8.416314621901603E-6 |
| 000382aa-6a83-4670-884e-c88fb34e96f6_MALDI2.txt.gz | 21346 | 5.7468747128377306E-5 |
| 0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz | 20825 | 1.9473923756235582E-5 |
| 00067955-90c8-42b2-b874-8e7ff2aa4d62_MALDI2.txt.gz | 21346 | 1.2452338865869532E-5 |
| 000bc9b6-1c49-48f7-9297-7d57431b760c_MALDI1.txt.gz | 20813 | 1.686271144356504E-5 |
(def driams-batch-all-pass?
(every? #(< (:max-diff %) 1e-4) driams-batch-results))driams-batch-all-pass?trueDRIAMS Peak Detection Comparison
Compare detected peaks between Ripple and MALDIquant on the same 10 DRIAMS spectra. We check that peak counts are within 5% and that peak masses align closely.
(defn compare-driams-peaks
"Compare peak detection between Ripple and MALDIquant on a DRIAMS spectrum."
[path]
(let [spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (map double (:intensity spec)))
;; Preprocess in R
_ (r `(<- dp (createMassSpectrum :mass ~masses :intensity ~intensities)))
_ (r "dp <- calibrateIntensity(removeBaseline(smoothIntensity(transformIntensity(dp, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5), method='SNIP', iterations=25), method='TIC')")
r-peak-masses (r->clj (r "mass(detectPeaks(dp, halfWindowSize=20, SNR=2, method='MAD'))"))
r-peak-count (count r-peak-masses)
;; Preprocess in Clojure
clj-processed (signal/preprocess-spectrum-data
{:mass masses :intensity intensities}
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 1
:should-tic-normalize true
:tic-target 1.0})
clj-peaks (signal/detect-peaks (:intensity clj-processed)
{:half-window-size 20 :snr 2})
clj-peak-count (count clj-peaks)
count-diff (Math/abs (- clj-peak-count r-peak-count))
pct-diff (if (pos? r-peak-count)
(* 100.0 (/ (double count-diff) r-peak-count))
0.0)]
{:file (last (clojure.string/split path #"/"))
:r-peaks r-peak-count
:clj-peaks clj-peak-count
:count-diff count-diff
:pct-diff (double (/ (Math/round (* pct-diff 10.0)) 10.0))}))(def driams-peak-results
(mapv compare-driams-peaks driams-batch-files))(kind/table (tc/dataset driams-peak-results))| file | r-peaks | clj-peaks | count-diff | pct-diff |
|---|---|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 452 | 452 | 0 | 0.0 |
| 0000ecf4-e821-4772-acb5-87db759edcae_MALDI2.txt.gz | 211 | 211 | 0 | 0.0 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 252 | 252 | 0 | 0.0 |
| 000297cd-7cea-421b-ac23-b1a095a7c976_MALDI1.txt.gz | 376 | 375 | 1 | 0.3 |
| 0002ec87-db65-42fb-910e-ebc5fe4429cf_MALDI2.txt.gz | 178 | 178 | 0 | 0.0 |
| 00034169-ded0-4516-8870-6a39a0505390_MALDI2.txt.gz | 245 | 245 | 0 | 0.0 |
| 000382aa-6a83-4670-884e-c88fb34e96f6_MALDI2.txt.gz | 188 | 188 | 0 | 0.0 |
| 0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz | 288 | 288 | 0 | 0.0 |
| 00067955-90c8-42b2-b874-8e7ff2aa4d62_MALDI2.txt.gz | 205 | 205 | 0 | 0.0 |
| 000bc9b6-1c49-48f7-9297-7d57431b760c_MALDI1.txt.gz | 349 | 348 | 1 | 0.3 |
Peak counts should be close (within 10%) — small differences arise from SG edge effects propagating through the pipeline.
(def driams-peaks-close?
(every? #(<= (:pct-diff %) 10.0) driams-peak-results))driams-peaks-close?trueDRIAMS Cross-Site Comparison
Load one spectrum from each of the four DRIAMS sites (A, B, C, D) and verify the pipeline produces matching results. This exercises spectra from different Bruker instruments and acquisition settings.
(def driams-cross-site-files
{"DRIAMS-A" "DRIAMS/DRIAMS-A/raw/2018/00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz"
"DRIAMS-B" "DRIAMS/DRIAMS-B/raw/2018/001c791c-76b3-4281-815e-ef33b2163647.txt.gz"
"DRIAMS-C" "DRIAMS/DRIAMS-C/raw/2018/08020709420_H1.txt.gz"
"DRIAMS-D" "DRIAMS/DRIAMS-D/raw/2018/0000c4b9-2471-4bc7-b13a-47b2a174928b_3312.txt.gz"})(def driams-cross-site-results
(mapv (fn [[site path]]
(let [result (compare-driams-full-pipeline path)]
(assoc result :site site)))
driams-cross-site-files))(kind/table (tc/dataset driams-cross-site-results))| file | n-points | max-diff | site |
|---|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 20813 | 2.472680221974346E-5 | DRIAMS-A |
| 001c791c-76b3-4281-815e-ef33b2163647.txt.gz | 20808 | 1.9447067308343624E-5 | DRIAMS-B |
| 08020709420_H1.txt.gz | 20558 | 2.67697432309341E-6 | DRIAMS-C |
| 0000c4b9-2471-4bc7-b13a-47b2a174928b_3312.txt.gz | 21348 | 1.7996115740437574E-5 | DRIAMS-D |
(def driams-cross-site-all-pass?
(every? #(< (:max-diff %) 1e-4) driams-cross-site-results))Moving Average Smoothing
MALDIquant’s smoothIntensity(method="MovingAverage") uses a simple running mean. Interior points use the full window; edge points replicate the first/last full-window mean (via the coefficient matrix approach shared with Savitzky-Golay).
Synthetic test
(def ma-test-input [10.0 1.0 5.0 3.0 8.0 2.0 7.0 4.0 6.0 9.0])(def r-ma-result
(r->clj (r "MALDIquant:::.movingAverage(c(10, 1, 5, 3, 8, 2, 7, 4, 6, 9), halfWindowSize=2)")))(def clj-ma-result
(vec (signal/moving-average-smooth ma-test-input {:half-window-size 2})))(def ma-synthetic-diff
(apply max (map #(Math/abs (- %1 %2)) r-ma-result clj-ma-result))){:r r-ma-result :clj clj-ma-result :max-diff ma-synthetic-diff}{:r
[5.4
5.4
5.4
3.8000000000000003
5.0
4.800000000000001
5.4
5.6000000000000005
5.6000000000000005
5.6000000000000005],
:clj [5.4 5.4 5.4 3.8 5.0 4.8 5.4 5.6 5.6 5.6],
:max-diff 8.881784197001252E-16}Real data (fiedler2009subset)
(def r-ma-real
(r->clj (r "intensity(smoothIntensity(fiedler2009subset[[1]], method='MovingAverage', halfWindowSize=10))")))(def clj-ma-real
(vec (signal/moving-average-smooth real-intensities {:half-window-size 10})))(def ma-real-diff
(apply max (map #(Math/abs (- (double %1) (double %2))) r-ma-real clj-ma-real)))ma-real-diff2.9103830456733704E-11TopHat Baseline Removal
The TopHat baseline uses mathematical morphology: signal - opening(signal), where opening = dilation(erosion(signal)). This removes broad baseline features while preserving narrow peaks.
Synthetic test
(def th-test-input [1.0 5.0 2.0 8.0 3.0 10.0 1.0 4.0 9.0 2.0 6.0 3.0 7.0 1.0 8.0 5.0 2.0 9.0 4.0 6.0])(def r-th-baseline
(r->clj (r (str "MALDIquant:::.estimateBaselineTopHat(1:20, c("
(clojure.string/join "," th-test-input) "), halfWindowSize=2)"))))(def r-th-corrected (mapv #(max 0.0 (- %1 %2)) th-test-input r-th-baseline))(def clj-th-corrected (vec (signal/tophat-baseline th-test-input {:half-window-size 2})))(def th-synthetic-diff
(apply max (map #(Math/abs (- %1 %2)) r-th-corrected clj-th-corrected)))th-synthetic-diff0.0Real data (fiedler2009subset)
(def r-th-real
(r->clj (r "intensity(removeBaseline(fiedler2009subset[[1]], method='TopHat', halfWindowSize=100))")))(def clj-th-real
(vec (signal/tophat-baseline real-intensities {:half-window-size 100})))(def th-real-diff
(apply max (map #(Math/abs (- (double %1) (double %2))) r-th-real clj-th-real)))th-real-diff0.0Median Calibration
MALDIquant’s calibrateIntensity(method="median") divides all intensities by the median, so the median of the calibrated spectrum is 1.0.
Synthetic test
(def r-medcal-result
(r->clj (r "intensity(calibrateIntensity(createMassSpectrum(1:10, as.double(1:10)), method='median'))")))(def clj-medcal-result
(vec (signal/median-calibrate (mapv double (range 1 11)) {})))(def medcal-diff
(apply max (map #(Math/abs (- %1 %2)) r-medcal-result clj-medcal-result)))medcal-diff0.0Real data (fiedler2009subset)
(def r-medcal-real
(r->clj (r "intensity(calibrateIntensity(fiedler2009subset[[1]], method='median'))")))(def clj-medcal-real
(vec (signal/median-calibrate real-intensities {})))(def medcal-real-diff
(apply max (map #(Math/abs (- (double %1) (double %2))) r-medcal-real clj-medcal-real)))medcal-real-diff0.0Trim
MALDIquant’s trim restricts a spectrum to a given mass range. Both endpoints are inclusive.
(def r-trim-count
(first (r->clj (r "length(trim(fiedler2009subset[[1]], range=c(4000, 8000)))"))))(def clj-trimmed
(signal/trim-spectrum
(tc/dataset {:mass real-masses :intensity real-intensities})
{:range [4000 8000]}))(def trim-match?
(= r-trim-count (tc/row-count clj-trimmed))){:r-count r-trim-count :clj-count (tc/row-count clj-trimmed) :match? trim-match?}{:r-count 16237, :clj-count 16237, :match? true}Mass range should match
(def r-trim-range
(r->clj (r "range(mass(trim(fiedler2009subset[[1]], range=c(4000, 8000))))")))(def clj-trim-range
[(first (:mass clj-trimmed)) (last (:mass clj-trimmed))])(def trim-range-match?
(every? #(< (Math/abs %) 1e-10)
(map - r-trim-range clj-trim-range))){:r-range r-trim-range :clj-range clj-trim-range :match? trim-range-match?}{:r-range [4000.1234819641822 7999.73222131455],
:clj-range [4000.1234819641822 7999.73222131455],
:match? true}Multi-Sample Peak Binning & Filtering
MALDIquant’s binPeaks(method='strict') clusters peaks across spectra by recursive gap-splitting. filterPeaks removes low-frequency peaks. We verify that Ripple’s bin-peaks-multi and filter-peaks-multi produce identical results when given the same detected peaks.
Get all 16 spectra, detect peaks with R, then compare binning/filtering.
(r "spectra_ms <- fiedler2009subset")$sPankreas_HB_L_061019_G10.M19.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 5 - 101840
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
$sPankreas_HB_L_061019_G10.M20.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 6 - 111862
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
$sPankreas_HB_L_061019_H7.O14.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 10 - 92807
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
$sPankreas_HB_L_061019_H7.P13.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3 - 94368
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
$sPankreas_HB_L_061019_F10.L19.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 21 - 100220
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
$sPankreas_HB_L_061019_F10.L20.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 8 - 87928
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
$sPankreas_HB_L_061019_F9.L17.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 28 - 99022
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
$sPankreas_HB_L_061019_F9.L18.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 32 - 110268
Memory usage : 506.359 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
$sPankreas_HB_L_061019_A6.A11.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 23 - 61160
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
$sPankreas_HB_L_061019_A6.A12.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 46 - 54383
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
$sPankreas_HB_L_061019_A8.A15.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 36 - 84852
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
$sPankreas_HB_L_061019_A8.A16.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 75 - 86455
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
$sPankreas_HB_L_061019_C4.F7.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 15 - 23448
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
$sPankreas_HB_L_061019_C4.F8.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 36 - 30171
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
$sPankreas_HB_L_061019_D9.G17.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 8 - 32828
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
$sPankreas_HB_L_061019_D9.G18.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 7 - 22786
Memory usage : 506.367 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
(r "spectra_ms <- transformIntensity(spectra_ms, method='sqrt')")$sPankreas_HB_L_061019_G10.M19.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 2.236e+00 - 3.191e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
$sPankreas_HB_L_061019_G10.M20.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 2.449e+00 - 3.345e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
$sPankreas_HB_L_061019_H7.O14.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3.162e+00 - 3.046e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
$sPankreas_HB_L_061019_H7.P13.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 1.732e+00 - 3.072e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
$sPankreas_HB_L_061019_F10.L19.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 4.583e+00 - 3.166e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
$sPankreas_HB_L_061019_F10.L20.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 2.828e+00 - 2.965e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
$sPankreas_HB_L_061019_F9.L17.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 5.292e+00 - 3.147e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
$sPankreas_HB_L_061019_F9.L18.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 5.657e+00 - 3.321e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
$sPankreas_HB_L_061019_A6.A11.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 4.796e+00 - 2.473e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
$sPankreas_HB_L_061019_A6.A12.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 6.782e+00 - 2.332e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
$sPankreas_HB_L_061019_A8.A15.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 6e+00 - 2.913e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
$sPankreas_HB_L_061019_A8.A16.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 8.66e+00 - 2.94e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
$sPankreas_HB_L_061019_C4.F7.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3.873e+00 - 1.531e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
$sPankreas_HB_L_061019_C4.F8.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 6e+00 - 1.737e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
$sPankreas_HB_L_061019_D9.G17.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 2.828e+00 - 1.812e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
$sPankreas_HB_L_061019_D9.G18.T_0209513_0020740_18
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 2.646e+00 - 1.51e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
(r "spectra_ms <- smoothIntensity(spectra_ms, method='SavitzkyGolay', halfWindowSize=5)")[[1]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 2.784e+00 - 3.192e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
[[2]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3.052e+00 - 3.345e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
[[3]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3.542e+00 - 3.048e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
[[4]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 2.041e+00 - 3.072e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
[[5]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 5.214e+00 - 3.165e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
[[6]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3.077e+00 - 2.966e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
[[7]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 5.72e+00 - 3.147e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
[[8]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 6.209e+00 - 3.321e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
[[9]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 5.404e+00 - 2.474e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
[[10]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 7.002e+00 - 2.331e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
[[11]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 6.552e+00 - 2.912e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
[[12]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 9.078e+00 - 2.941e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
[[13]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 4.62e+00 - 1.532e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
[[14]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 6.524e+00 - 1.736e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
[[15]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3.311e+00 - 1.811e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
[[16]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 3.208e+00 - 1.509e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
(r "spectra_ms <- removeBaseline(spectra_ms, method='SNIP', iterations=25)")[[1]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.742e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
[[2]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.835e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
[[3]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.933e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
[[4]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 2.049e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
[[5]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.891e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
[[6]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.824e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
[[7]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.657e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
[[8]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 2.064e+02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
[[9]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.419e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
[[10]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.257e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
[[11]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.632e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
[[12]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.54e+02
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
[[13]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 7.365e+01
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
[[14]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 8.791e+01
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
[[15]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 8.805e+01
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
[[16]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 6.524e+01
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
(r "spectra_ms <- calibrateIntensity(spectra_ms, method='TIC')")[[1]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 8.461e-03
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
[[2]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 7.852e-03
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
[[3]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.059e-02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
[[4]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.054e-02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
[[5]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 9.301e-03
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
[[6]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.053e-02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
[[7]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 8.824e-03
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
[[8]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 1.005e-02
Memory usage : 671.938 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
[[9]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 7.047e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
[[10]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 6.926e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
[[11]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 7.761e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
[[12]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 7.491e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
[[13]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 4.716e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
[[14]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 5.422e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
[[15]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 5.188e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
[[16]]
S4 class type : MassSpectrum
Number of m/z values : 42388
Range of m/z values : 1000.015 - 9999.734
Range of intensity values: 0e+00 - 3.999e-03
Memory usage : 671.945 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
(r "peaks_ms <- detectPeaks(spectra_ms, halfWindowSize=20, SNR=2)")[[1]]
S4 class type : MassPeaks
Number of m/z values : 330
Range of m/z values : 1011.366 - 9952.664
Range of intensity values: 7.723e-05 - 8.461e-03
Range of snr values : 2.002 - 219.315
Memory usage : 17.352 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
[[2]]
S4 class type : MassPeaks
Number of m/z values : 309
Range of m/z values : 1003.894 - 9861.421
Range of intensity values: 7.332e-05 - 7.852e-03
Range of snr values : 2.016 - 215.882
Memory usage : 16.859 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
[[3]]
S4 class type : MassPeaks
Number of m/z values : 355
Range of m/z values : 1010.854 - 9954.274
Range of intensity values: 8.204e-05 - 1.059e-02
Range of snr values : 2.005 - 258.951
Memory usage : 17.938 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
[[4]]
S4 class type : MassPeaks
Number of m/z values : 350
Range of m/z values : 1003.587 - 9964.905
Range of intensity values: 7.919e-05 - 1.054e-02
Range of snr values : 2 - 266.122
Memory usage : 17.82 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
[[5]]
S4 class type : MassPeaks
Number of m/z values : 299
Range of m/z values : 1005.222 - 9966.516
Range of intensity values: 7.68e-05 - 9.301e-03
Range of snr values : 2.009 - 243.316
Memory usage : 16.625 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
[[6]]
S4 class type : MassPeaks
Number of m/z values : 342
Range of m/z values : 1011.469 - 9975.863
Range of intensity values: 7.986e-05 - 1.053e-02
Range of snr values : 2.007 - 264.726
Memory usage : 17.633 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
[[7]]
S4 class type : MassPeaks
Number of m/z values : 325
Range of m/z values : 1004.507 - 9953.63
Range of intensity values: 7.686e-05 - 8.824e-03
Range of snr values : 2.002 - 229.869
Memory usage : 17.234 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
[[8]]
S4 class type : MassPeaks
Number of m/z values : 311
Range of m/z values : 1004.507 - 9819.796
Range of intensity values: 7.818e-05 - 1.005e-02
Range of snr values : 2.003 - 257.485
Memory usage : 16.906 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
[[9]]
S4 class type : MassPeaks
Number of m/z values : 309
Range of m/z values : 1011.059 - 9927.884
Range of intensity values: 8.147e-05 - 7.047e-03
Range of snr values : 2.016 - 174.37
Memory usage : 16.867 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
[[10]]
S4 class type : MassPeaks
Number of m/z values : 319
Range of m/z values : 1011.059 - 9980.377
Range of intensity values: 8.049e-05 - 6.926e-03
Range of snr values : 2.007 - 172.685
Memory usage : 17.102 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
[[11]]
S4 class type : MassPeaks
Number of m/z values : 275
Range of m/z values : 1004.303 - 9863.345
Range of intensity values: 8.159e-05 - 7.761e-03
Range of snr values : 2 - 190.292
Memory usage : 16.07 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
[[12]]
S4 class type : MassPeaks
Number of m/z values : 281
Range of m/z values : 1004.2 - 9941.397
Range of intensity values: 7.646e-05 - 7.491e-03
Range of snr values : 2.015 - 197.401
Memory usage : 16.211 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
[[13]]
S4 class type : MassPeaks
Number of m/z values : 329
Range of m/z values : 1012.392 - 9978.12
Range of intensity values: 9.634e-05 - 4.716e-03
Range of snr values : 2.001 - 97.932
Memory usage : 17.336 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
[[14]]
S4 class type : MassPeaks
Number of m/z values : 303
Range of m/z values : 1012.392 - 9978.12
Range of intensity values: 9.206e-05 - 5.422e-03
Range of snr values : 2.001 - 117.867
Memory usage : 16.727 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
[[15]]
S4 class type : MassPeaks
Number of m/z values : 309
Range of m/z values : 1011.674 - 9958.461
Range of intensity values: 9.146e-05 - 5.188e-03
Range of snr values : 2.002 - 113.559
Memory usage : 16.867 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
[[16]]
S4 class type : MassPeaks
Number of m/z values : 341
Range of m/z values : 1011.469 - 9948.8
Range of intensity values: 9.244e-05 - 3.999e-03
Range of snr values : 2.001 - 86.575
Memory usage : 17.617 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
(r "peaks_ms_binned <- binPeaks(peaks_ms, tolerance=0.002)")[[1]]
S4 class type : MassPeaks
Number of m/z values : 330
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 7.723e-05 - 8.461e-03
Range of snr values : 2.002 - 219.315
Memory usage : 17.352 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
[[2]]
S4 class type : MassPeaks
Number of m/z values : 309
Range of m/z values : 1004.317 - 9862.109
Range of intensity values: 7.332e-05 - 7.852e-03
Range of snr values : 2.016 - 215.882
Memory usage : 16.859 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
[[3]]
S4 class type : MassPeaks
Number of m/z values : 355
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 8.204e-05 - 1.059e-02
Range of snr values : 2.005 - 258.951
Memory usage : 17.938 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
[[4]]
S4 class type : MassPeaks
Number of m/z values : 350
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.919e-05 - 1.054e-02
Range of snr values : 2 - 266.122
Memory usage : 17.82 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
[[5]]
S4 class type : MassPeaks
Number of m/z values : 299
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.68e-05 - 9.301e-03
Range of snr values : 2.009 - 243.316
Memory usage : 16.625 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
[[6]]
S4 class type : MassPeaks
Number of m/z values : 342
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 7.986e-05 - 1.053e-02
Range of snr values : 2.007 - 264.726
Memory usage : 17.633 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
[[7]]
S4 class type : MassPeaks
Number of m/z values : 325
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.686e-05 - 8.824e-03
Range of snr values : 2.002 - 229.869
Memory usage : 17.234 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
[[8]]
S4 class type : MassPeaks
Number of m/z values : 311
Range of m/z values : 1004.317 - 9821.331
Range of intensity values: 7.818e-05 - 1.005e-02
Range of snr values : 2.003 - 257.485
Memory usage : 16.906 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
[[9]]
S4 class type : MassPeaks
Number of m/z values : 309
Range of m/z values : 1011.418 - 9928.206
Range of intensity values: 8.147e-05 - 7.047e-03
Range of snr values : 2.016 - 174.37
Memory usage : 16.867 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
[[10]]
S4 class type : MassPeaks
Number of m/z values : 319
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 8.049e-05 - 6.926e-03
Range of snr values : 2.007 - 172.685
Memory usage : 17.102 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
[[11]]
S4 class type : MassPeaks
Number of m/z values : 275
Range of m/z values : 1004.317 - 9862.109
Range of intensity values: 8.159e-05 - 7.761e-03
Range of snr values : 2 - 190.292
Memory usage : 16.07 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
[[12]]
S4 class type : MassPeaks
Number of m/z values : 281
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.646e-05 - 7.491e-03
Range of snr values : 2.015 - 197.401
Memory usage : 16.211 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
[[13]]
S4 class type : MassPeaks
Number of m/z values : 329
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 9.634e-05 - 4.716e-03
Range of snr values : 2.001 - 97.932
Memory usage : 17.336 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
[[14]]
S4 class type : MassPeaks
Number of m/z values : 303
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 9.206e-05 - 5.422e-03
Range of snr values : 2.001 - 117.867
Memory usage : 16.727 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
[[15]]
S4 class type : MassPeaks
Number of m/z values : 309
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 9.146e-05 - 5.188e-03
Range of snr values : 2.002 - 113.559
Memory usage : 16.867 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
[[16]]
S4 class type : MassPeaks
Number of m/z values : 341
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 9.244e-05 - 3.999e-03
Range of snr values : 2.001 - 86.575
Memory usage : 17.617 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
(r "peaks_ms_filt <- filterPeaks(peaks_ms_binned, minFrequency=0.25)")[[1]]
S4 class type : MassPeaks
Number of m/z values : 297
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 7.723e-05 - 8.461e-03
Range of snr values : 2.002 - 219.315
Memory usage : 16.578 KiB
Name : Pankreas_HB_L_061019_G10.M19
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m19/1/1SLin/fid
[[2]]
S4 class type : MassPeaks
Number of m/z values : 284
Range of m/z values : 1004.317 - 9862.109
Range of intensity values: 7.347e-05 - 7.852e-03
Range of snr values : 2.02 - 215.882
Memory usage : 16.273 KiB
Name : Pankreas_HB_L_061019_G10.M20
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_G10/0_m20/1/1SLin/fid
[[3]]
S4 class type : MassPeaks
Number of m/z values : 303
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 8.204e-05 - 1.059e-02
Range of snr values : 2.005 - 258.951
Memory usage : 16.719 KiB
Name : Pankreas_HB_L_061019_H7.O14
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_o14/1/1SLin/fid
[[4]]
S4 class type : MassPeaks
Number of m/z values : 303
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.919e-05 - 1.054e-02
Range of snr values : 2 - 266.122
Memory usage : 16.719 KiB
Name : Pankreas_HB_L_061019_H7.P13
File : /data/set A - discovery leipzig/control/Pankreas_HB_L_061019_H7/0_p13/1/1SLin/fid
[[5]]
S4 class type : MassPeaks
Number of m/z values : 281
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.68e-05 - 9.301e-03
Range of snr values : 2.009 - 243.316
Memory usage : 16.203 KiB
Name : Pankreas_HB_L_061019_F10.L19
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l19/1/1SLin/fid
[[6]]
S4 class type : MassPeaks
Number of m/z values : 288
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 7.989e-05 - 1.053e-02
Range of snr values : 2.008 - 264.726
Memory usage : 16.367 KiB
Name : Pankreas_HB_L_061019_F10.L20
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F10/0_l20/1/1SLin/fid
[[7]]
S4 class type : MassPeaks
Number of m/z values : 288
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.701e-05 - 8.824e-03
Range of snr values : 2.006 - 229.869
Memory usage : 16.367 KiB
Name : Pankreas_HB_L_061019_F9.L17
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l17/1/1SLin/fid
[[8]]
S4 class type : MassPeaks
Number of m/z values : 286
Range of m/z values : 1004.317 - 9821.331
Range of intensity values: 7.818e-05 - 1.005e-02
Range of snr values : 2.003 - 257.485
Memory usage : 16.32 KiB
Name : Pankreas_HB_L_061019_F9.L18
File : /data/set A - discovery leipzig/tumor/Pankreas_HB_L_061019_F9/0_l18/1/1SLin/fid
[[9]]
S4 class type : MassPeaks
Number of m/z values : 284
Range of m/z values : 1011.418 - 9928.206
Range of intensity values: 8.147e-05 - 7.047e-03
Range of snr values : 2.016 - 174.37
Memory usage : 16.281 KiB
Name : Pankreas_HB_L_061019_A6.A11
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a11/1/1SLin/fid
[[10]]
S4 class type : MassPeaks
Number of m/z values : 292
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 8.053e-05 - 6.926e-03
Range of snr values : 2.008 - 172.685
Memory usage : 16.469 KiB
Name : Pankreas_HB_L_061019_A6.A12
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A6/0_a12/1/1SLin/fid
[[11]]
S4 class type : MassPeaks
Number of m/z values : 263
Range of m/z values : 1004.317 - 9862.109
Range of intensity values: 8.159e-05 - 7.761e-03
Range of snr values : 2 - 190.292
Memory usage : 15.789 KiB
Name : Pankreas_HB_L_061019_A8.A15
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a15/1/1SLin/fid
[[12]]
S4 class type : MassPeaks
Number of m/z values : 270
Range of m/z values : 1004.317 - 9955.814
Range of intensity values: 7.646e-05 - 7.491e-03
Range of snr values : 2.015 - 197.401
Memory usage : 15.953 KiB
Name : Pankreas_HB_L_061019_A8.A16
File : /data/set B - discovery heidelberg/control/Pankreas_HB_L_061019_A8/0_a16/1/1SLin/fid
[[13]]
S4 class type : MassPeaks
Number of m/z values : 277
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 9.659e-05 - 4.716e-03
Range of snr values : 2.006 - 97.932
Memory usage : 16.117 KiB
Name : Pankreas_HB_L_061019_C4.F7
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f7/1/1SLin/fid
[[14]]
S4 class type : MassPeaks
Number of m/z values : 270
Range of m/z values : 1011.418 - 9978.12
Range of intensity values: 9.243e-05 - 5.422e-03
Range of snr values : 2.009 - 117.867
Memory usage : 15.953 KiB
Name : Pankreas_HB_L_061019_C4.F8
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_C4/0_f8/1/1SLin/fid
[[15]]
S4 class type : MassPeaks
Number of m/z values : 281
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 9.146e-05 - 5.188e-03
Range of snr values : 2.002 - 113.559
Memory usage : 16.211 KiB
Name : Pankreas_HB_L_061019_D9.G17
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g17/1/1SLin/fid
[[16]]
S4 class type : MassPeaks
Number of m/z values : 288
Range of m/z values : 1011.418 - 9955.814
Range of intensity values: 9.244e-05 - 3.999e-03
Range of snr values : 2.001 - 86.575
Memory usage : 16.375 KiB
Name : Pankreas_HB_L_061019_D9.G18
File : /data/set B - discovery heidelberg/tumor/Pankreas_HB_L_061019_D9/0_g18/1/1SLin/fid
Load R’s detected peaks into Clojure
(def ms-peak-lists
(mapv (fn [i]
(let [masses (r->clj (r (format "mass(peaks_ms[[%d]])" (inc i))))
intens (r->clj (r (format "intensity(peaks_ms[[%d]])" (inc i))))]
{:masses (double-array masses)
:intensities (double-array intens)}))
(range 16)))Run Ripple’s binning + filtering
(def clj-ms-binned (maldi/bin-peaks-multi ms-peak-lists {:tolerance 0.002}))(def clj-ms-filtered (maldi/filter-peaks-multi clj-ms-binned {:min-frequency 0.25}))Compare filtered peak masses per spectrum
(def bin-filter-comparison
(mapv (fn [i]
(let [r-masses (sort (r->clj (r (format "mass(peaks_ms_filt[[%d]])" (inc i)))))
clj-masses (sort (seq (:masses (nth clj-ms-filtered i))))
n-r (count r-masses)
n-clj (count clj-masses)]
(if (= n-r n-clj)
(let [diffs (map #(Math/abs (- %1 %2)) r-masses clj-masses)]
{:spectrum (inc i) :n n-r :max-diff (apply max diffs) :match? true})
{:spectrum (inc i) :n-r n-r :n-clj n-clj :max-diff ##Inf :match? false})))
(range 16)))(kind/table
{:column-names ["Spectrum" "# Peaks" "Max Mass Diff" "Match?"]
:row-vectors (mapv (fn [{:keys [spectrum n max-diff match?]}]
[spectrum n max-diff match?])
bin-filter-comparison)})| Spectrum | # Peaks | Max Mass Diff | Match? |
|---|---|---|---|
| 1 | 297 | 0.0 | true |
| 2 | 284 | 0.0 | true |
| 3 | 303 | 0.0 | true |
| 4 | 303 | 0.0 | true |
| 5 | 281 | 0.0 | true |
| 6 | 288 | 0.0 | true |
| 7 | 288 | 0.0 | true |
| 8 | 286 | 0.0 | true |
| 9 | 284 | 0.0 | true |
| 10 | 292 | 0.0 | true |
| 11 | 263 | 0.0 | true |
| 12 | 270 | 0.0 | true |
| 13 | 277 | 0.0 | true |
| 14 | 270 | 0.0 | true |
| 15 | 281 | 0.0 | true |
| 16 | 288 | 0.0 | true |
Feature matrix dimensions
(def clj-ms-im (maldi/intensity-matrix clj-ms-filtered))(r "fm_ms <- intensityMatrix(peaks_ms_filt, spectra_ms)") 1004.31720255431 1011.41754304887 1020.64268860656 1029.54760952953
[1,] 6.233392e-05 0.0002602160 0.0024728537 7.830662e-05
[2,] 8.554203e-05 0.0001879262 0.0025690551 6.562547e-05
[3,] 3.914342e-05 0.0002638925 0.0023647329 9.662895e-05
[4,] 8.517391e-05 0.0002080950 0.0024858294 4.633633e-05
[5,] 1.081112e-04 0.0001633728 0.0045228095 3.464811e-05
[6,] 5.442202e-05 0.0001326696 0.0046013557 1.924827e-05
[7,] 1.145109e-04 0.0001329368 0.0045771893 7.239133e-06
[8,] 1.157325e-04 0.0002122369 0.0036665889 1.490200e-05
[9,] 2.346302e-05 0.0004036583 0.0016703505 1.548920e-04
[10,] 4.868738e-05 0.0002938050 0.0016099362 8.333884e-05
[11,] 9.199356e-05 0.0003258199 0.0030502944 9.902513e-05
[12,] 8.308118e-05 0.0002926303 0.0031692064 9.194140e-05
[13,] 4.577808e-05 0.0003744563 0.0008272124 1.378556e-04
[14,] 3.361890e-05 0.0004487872 0.0009947862 2.441261e-04
[15,] 6.565656e-05 0.0004379729 0.0011653420 1.981116e-04
[16,] 3.259001e-05 0.0003826989 0.0007813896 1.212823e-04
1041.14932756537 1046.7663626786 1057.94279608053 1065.43462794082
[1,] 0.0001563224 1.513257e-04 0.0003286716 1.237163e-04
[2,] 0.0001770930 1.251498e-04 0.0002850785 8.384954e-05
[3,] 0.0002559284 5.324558e-05 0.0003248063 1.224491e-04
[4,] 0.0002154635 1.104351e-04 0.0003181782 1.104443e-04
[5,] 0.0001620963 8.367968e-05 0.0002807123 4.255064e-05
[6,] 0.0001635148 5.512708e-05 0.0002394700 6.296348e-05
[7,] 0.0001959407 5.802523e-05 0.0003090169 6.801047e-05
[8,] 0.0001745536 7.843047e-05 0.0003328850 1.555967e-04
[9,] 0.0002552499 5.814560e-05 0.0003266939 1.455424e-04
[10,] 0.0002431024 3.408683e-05 0.0002757072 9.280216e-05
[11,] 0.0002865086 4.578771e-05 0.0003777966 1.235420e-04
[12,] 0.0002500487 1.081103e-04 0.0003252578 9.088626e-05
[13,] 0.0002346907 3.033363e-05 0.0002874353 4.740105e-05
[14,] 0.0003451765 1.091792e-04 0.0004143636 1.376139e-04
[15,] 0.0002807287 1.336690e-04 0.0003792295 1.427437e-04
[16,] 0.0002172119 1.240304e-04 0.0003531281 8.590260e-05
1077.51529448242 1083.76841305565 1102.58813238612 1113.69863254127
[1,] 0.0010900489 0.0002655801 0.0002016598 0.0001444655
[2,] 0.0012242805 0.0002287196 0.0001846826 0.0001591388
[3,] 0.0009669642 0.0003476084 0.0002245598 0.0002590271
[4,] 0.0010705210 0.0001394887 0.0001393718 0.0002058321
[5,] 0.0010955031 0.0002285259 0.0001997255 0.0001479320
[6,] 0.0010402199 0.0002412138 0.0001858447 0.0001137480
[7,] 0.0011902558 0.0003271785 0.0001964737 0.0001604190
[8,] 0.0008572193 0.0002681306 0.0001971960 0.0002164918
[9,] 0.0004970815 0.0003319338 0.0002336007 0.0002103521
[10,] 0.0004566414 0.0003267494 0.0001837020 0.0001547070
[11,] 0.0007813341 0.0003888134 0.0002115152 0.0002068560
[12,] 0.0007857943 0.0003610264 0.0002253071 0.0001995704
[13,] 0.0002599898 0.0003463948 0.0001906822 0.0002219617
[14,] 0.0003297822 0.0003713634 0.0003038474 0.0002340092
[15,] 0.0004858923 0.0004345545 0.0002545065 0.0002198892
[16,] 0.0002878878 0.0003583872 0.0002304426 0.0002200712
1127.50965216907 1131.44488195715 1139.39759618823 1151.03886424966
[1,] 2.132939e-04 0.0002784396 3.536721e-05 1.139989e-04
[2,] 1.539865e-04 0.0003176652 3.552299e-05 9.179705e-05
[3,] 1.403083e-04 0.0002045894 5.499805e-05 1.076957e-04
[4,] 1.278800e-04 0.0002870032 2.872101e-05 9.669681e-05
[5,] 9.606181e-05 0.0002285187 9.324580e-05 7.912329e-05
[6,] 1.309413e-04 0.0002109544 8.747974e-05 7.328133e-05
[7,] 1.318661e-04 0.0002397461 5.595834e-05 2.901041e-05
[8,] 2.121976e-04 0.0002189332 8.101083e-05 8.895353e-05
[9,] 1.796826e-04 0.0002430891 2.086874e-05 9.035051e-05
[10,] 1.424134e-04 0.0001902625 6.128006e-05 1.010497e-04
[11,] 1.406696e-04 0.0002610711 1.038072e-04 1.134349e-04
[12,] 1.589366e-04 0.0002345974 3.697675e-05 5.858938e-05
[13,] 1.859924e-04 0.0001998928 6.345493e-05 1.163060e-04
[14,] 2.582896e-04 0.0002425313 7.768487e-05 1.231286e-04
[15,] 2.684688e-04 0.0003522635 8.017190e-05 1.115611e-04
[16,] 1.191915e-04 0.0001978248 5.758174e-05 1.430374e-04
1157.64842347203 1169.25301655303 1175.80247967962 1181.96836514399
[1,] 0.0002698548 5.565522e-05 0.0002639369 9.368569e-05
[2,] 0.0003034228 5.371508e-05 0.0002661083 2.147111e-05
[3,] 0.0003452546 1.479356e-04 0.0002959358 1.495365e-05
[4,] 0.0003223691 4.798200e-05 0.0002589972 3.312572e-05
[5,] 0.0002575577 4.454367e-05 0.0002011908 4.404029e-05
[6,] 0.0002196692 1.110950e-04 0.0002194555 2.759023e-05
[7,] 0.0002660382 8.100208e-05 0.0002154292 2.958167e-05
[8,] 0.0003246103 6.578613e-05 0.0002791982 8.060064e-05
[9,] 0.0004088585 2.694981e-05 0.0003690656 1.022393e-04
[10,] 0.0003491593 1.255185e-04 0.0003474479 8.731457e-05
[11,] 0.0004950415 6.319325e-05 0.0003549689 7.361874e-05
[12,] 0.0004190820 4.641010e-05 0.0003036775 1.935850e-05
[13,] 0.0003871616 1.087096e-04 0.0002425346 1.633530e-04
[14,] 0.0005359613 1.059363e-04 0.0004512868 1.185738e-04
[15,] 0.0004961199 1.362663e-04 0.0004295048 1.196050e-04
[16,] 0.0004177367 1.176107e-04 0.0002698576 1.274322e-04
1187.90337593183 1191.46662654296 1199.71247447263 1206.77228039543
[1,] 1.640023e-04 1.111297e-04 1.862240e-05 0.007627510
[2,] 1.244116e-04 1.949847e-04 4.796481e-05 0.007852467
[3,] 1.245299e-04 1.454910e-04 3.347541e-05 0.007186828
[4,] 1.625531e-04 1.052459e-04 2.190087e-05 0.007117237
[5,] 1.596515e-04 2.334011e-04 2.742599e-06 0.007945035
[6,] 1.953840e-04 2.362862e-04 2.289840e-05 0.008225812
[7,] 2.433665e-04 1.496866e-04 2.923396e-07 0.008580828
[8,] 1.329416e-04 9.431588e-05 3.741565e-06 0.007279374
[9,] 1.183939e-04 1.069869e-05 3.487013e-05 0.004723771
[10,] 9.609998e-05 0.000000e+00 8.926801e-05 0.004552265
[11,] 9.957890e-05 3.588898e-05 6.485197e-05 0.006071684
[12,] 1.162105e-04 2.532893e-05 6.672502e-05 0.006279015
[13,] 1.166523e-04 2.956914e-05 1.126483e-04 0.002386118
[14,] 1.401377e-04 7.884653e-06 1.559475e-04 0.002788868
[15,] 1.999892e-04 1.333060e-05 1.526921e-04 0.002988588
[16,] 1.367983e-04 2.623430e-05 1.100883e-04 0.002339595
1220.6120095476 1224.67510341494 1227.63551769667 1229.9569906765
[1,] 1.070090e-04 0.0002016726 8.588368e-05 1.345082e-04
[2,] 1.044987e-04 0.0001389883 7.199423e-05 1.171554e-04
[3,] 1.366694e-04 0.0001993265 1.323357e-04 1.538637e-04
[4,] 1.873767e-04 0.0001918174 8.197956e-05 3.649623e-05
[5,] 1.276794e-04 0.0001156266 1.096011e-04 1.505700e-04
[6,] 1.121197e-04 0.0001777165 1.106832e-04 8.394558e-05
[7,] 5.593568e-05 0.0001174511 9.170132e-05 7.880993e-05
[8,] 5.207031e-05 0.0001928616 1.291298e-04 8.505778e-05
[9,] 8.177093e-05 0.0001495417 7.493632e-05 1.187434e-04
[10,] 1.232910e-05 0.0001349204 1.283252e-04 9.636592e-05
[11,] 1.402973e-05 0.0001493642 2.047641e-04 1.544357e-04
[12,] 2.821608e-06 0.0001913388 1.893915e-04 1.561522e-04
[13,] 1.991891e-04 0.0002107033 9.651368e-05 7.720384e-05
[14,] 1.594112e-04 0.0002090770 8.687777e-05 1.074302e-04
[15,] 1.953781e-04 0.0002825794 1.113637e-04 9.453409e-05
[16,] 1.830271e-04 0.0002104337 8.795686e-05 8.866461e-05
1237.3663658192 1244.55735932097 1254.9431158931 1263.61464996386
[1,] 5.976981e-05 0.0002040738 5.244896e-05 0.0026106257
[2,] 3.039343e-05 0.0001877864 2.116073e-05 0.0028313960
[3,] 7.630476e-05 0.0001658468 6.873952e-05 0.0016813270
[4,] 1.318308e-04 0.0001551729 8.188650e-05 0.0018577055
[5,] 1.180231e-04 0.0001828915 4.283505e-05 0.0012974794
[6,] 1.067475e-04 0.0001313562 8.271799e-06 0.0012554321
[7,] 7.877883e-05 0.0001960098 7.568511e-05 0.0013792277
[8,] 1.093405e-04 0.0001822382 8.169430e-05 0.0010597469
[9,] 2.824085e-05 0.0001445951 1.536442e-04 0.0006753581
[10,] 5.321757e-05 0.0001142853 1.037456e-04 0.0006790600
[11,] 4.626677e-05 0.0001923224 9.556236e-05 0.0008355969
[12,] 8.266677e-05 0.0001844338 8.337209e-05 0.0008739484
[13,] 1.607210e-04 0.0001765288 1.559829e-04 0.0002943664
[14,] 2.107125e-04 0.0001833813 1.751712e-04 0.0003430184
[15,] 1.809066e-04 0.0001622765 1.689726e-04 0.0003572904
[16,] 1.415483e-04 0.0001339781 1.400255e-04 0.0002214251
1273.44936464953 1279.54784129716 1291.89268450468 1299.09652486703
[1,] 4.261048e-05 1.154759e-04 1.931417e-04 0.0002511111
[2,] 1.907603e-05 8.441546e-05 1.517570e-04 0.0001971683
[3,] 1.714704e-05 8.855524e-05 1.644648e-04 0.0003382667
[4,] 9.630847e-06 8.400998e-05 1.900034e-04 0.0002497189
[5,] 3.100216e-05 2.912371e-05 2.205578e-04 0.0001666393
[6,] 3.574327e-05 8.665755e-05 2.074249e-04 0.0002243320
[7,] 3.859941e-05 4.396432e-05 2.474597e-04 0.0002795921
[8,] 1.258506e-05 1.000710e-04 2.336167e-04 0.0003379669
[9,] 4.409858e-05 1.245351e-04 1.440598e-04 0.0003999395
[10,] 4.910494e-05 1.107601e-04 1.444064e-04 0.0003011835
[11,] 1.009231e-04 8.498786e-05 1.024271e-04 0.0003456596
[12,] 1.140217e-04 1.027879e-04 7.648603e-05 0.0003492094
[13,] 1.637247e-04 1.651620e-04 2.952881e-04 0.0003837521
[14,] 1.220087e-04 1.790334e-04 2.926843e-04 0.0004348250
[15,] 1.446807e-04 9.323943e-05 3.263788e-04 0.0003829054
[16,] 1.178057e-04 1.221770e-04 3.038253e-04 0.0004110207
1309.30149397579 1317.84841020397 1329.18467000337 1334.50133292741
[1,] 0.0003124950 0.0002923181 0.0001873845 1.831600e-04
[2,] 0.0002920095 0.0002606458 0.0001343630 2.077710e-04
[3,] 0.0001315256 0.0002173461 0.0001654305 7.772483e-05
[4,] 0.0001642407 0.0001979489 0.0001678609 1.213162e-04
[5,] 0.0002601720 0.0001820429 0.0001917179 1.361057e-04
[6,] 0.0002494913 0.0001601947 0.0002443062 1.242624e-04
[7,] 0.0001042784 0.0002171035 0.0001483715 1.636534e-04
[8,] 0.0001574500 0.0001871263 0.0001721291 1.313121e-04
[9,] 0.0001612567 0.0002174944 0.0002425704 6.836256e-05
[10,] 0.0001117840 0.0001515415 0.0001933331 1.729396e-05
[11,] 0.0001199085 0.0002038992 0.0003056473 1.014578e-04
[12,] 0.0001244028 0.0001471164 0.0002713590 8.766581e-05
[13,] 0.0001290196 0.0001673310 0.0002437379 5.010543e-05
[14,] 0.0002200019 0.0002002380 0.0002638215 1.302578e-05
[15,] 0.0001541793 0.0001945632 0.0002105740 7.714852e-05
[16,] 0.0001450560 0.0001626811 0.0001621972 6.484288e-05
1350.9728296865 1363.75277550783 1371.51970966021 1378.89418632839
[1,] 0.0060918771 4.905480e-06 0.0002809212 5.229111e-04
[2,] 0.0063546982 6.336950e-05 0.0002269331 4.948243e-04
[3,] 0.0041677639 0.000000e+00 0.0002902330 2.788382e-04
[4,] 0.0041636861 8.218275e-05 0.0002217449 3.396303e-04
[5,] 0.0034014197 3.285776e-05 0.0001771584 4.252320e-04
[6,] 0.0034096394 4.353821e-05 0.0002152807 4.862690e-04
[7,] 0.0039655811 6.175823e-05 0.0001883450 3.854689e-04
[8,] 0.0031703185 2.243043e-05 0.0002560292 3.170934e-04
[9,] 0.0023566155 3.913130e-05 0.0003475276 9.108745e-05
[10,] 0.0023593129 2.837248e-05 0.0003302591 1.528853e-04
[11,] 0.0024873303 1.224032e-05 0.0003881486 1.956923e-04
[12,] 0.0026387644 5.700259e-05 0.0003202534 2.011336e-04
[13,] 0.0008577658 7.721085e-05 0.0004129601 5.182369e-05
[14,] 0.0011066077 1.300066e-04 0.0004812261 8.928824e-05
[15,] 0.0012941459 1.076891e-04 0.0004052823 1.258707e-04
[16,] 0.0009374053 9.576274e-05 0.0003569566 1.192206e-04
1381.53743776043 1389.91382161461 1396.36010888985 1400.80706851018
[1,] 3.431213e-04 0.0002628130 3.027678e-04 4.887844e-05
[2,] 2.567227e-04 0.0002680163 2.762684e-04 2.184506e-05
[3,] 1.061438e-04 0.0002622659 1.327035e-04 3.016483e-05
[4,] 1.416981e-04 0.0002568889 1.680603e-04 3.588521e-05
[5,] 2.441166e-04 0.0002790884 1.917134e-04 1.646274e-05
[6,] 2.190946e-04 0.0003120526 2.300449e-04 1.900240e-05
[7,] 1.934880e-04 0.0002368148 8.094637e-05 2.372537e-06
[8,] 1.864863e-04 0.0002214198 1.065248e-04 4.266995e-05
[9,] 4.056640e-06 0.0002160063 1.077065e-04 1.430484e-04
[10,] 1.099319e-04 0.0002181711 5.519168e-05 8.070760e-05
[11,] 9.659689e-05 0.0002936984 1.998001e-04 7.000255e-05
[12,] 1.250475e-04 0.0002994768 1.222240e-04 1.268729e-04
[13,] 1.334429e-04 0.0002094515 1.875389e-04 6.867756e-05
[14,] 1.209302e-04 0.0002452906 1.083191e-04 1.078984e-04
[15,] 1.817902e-04 0.0002213439 1.515448e-04 1.583226e-04
[16,] 1.328015e-04 0.0001829501 1.882440e-04 1.702251e-04
1404.48482242712 1413.92152319917 1419.91387758083 1426.41508759298
[1,] 3.150884e-04 5.346928e-05 0.0004507086 3.641152e-05
[2,] 3.488844e-04 6.343452e-05 0.0004988649 8.225068e-05
[3,] 1.556464e-04 1.685414e-04 0.0003291924 9.419160e-06
[4,] 1.613981e-04 6.551993e-05 0.0002542797 9.666209e-05
[5,] 1.924226e-04 4.733973e-05 0.0003657733 4.395915e-05
[6,] 1.661398e-04 8.539699e-05 0.0003214705 2.125716e-05
[7,] 2.401993e-04 1.006103e-04 0.0003198816 4.028895e-05
[8,] 1.625037e-04 6.951496e-05 0.0003231508 6.697865e-05
[9,] 6.474129e-05 1.302910e-04 0.0002501342 7.578484e-06
[10,] 4.477344e-05 8.145421e-05 0.0001904855 9.538495e-05
[11,] 1.834608e-04 7.101355e-05 0.0003842632 5.237092e-05
[12,] 7.646136e-05 7.801980e-05 0.0003612819 1.394607e-05
[13,] 1.063049e-04 1.183602e-04 0.0002229672 1.136419e-04
[14,] 1.502413e-04 1.632336e-04 0.0002950487 1.080940e-04
[15,] 2.524913e-05 1.559849e-04 0.0002840895 7.015297e-05
[16,] 6.797536e-05 1.317186e-04 0.0002326914 1.500964e-04
1431.95374321002 1435.10659359104 1449.72011015482 1466.00478125297
[1,] 2.576044e-05 1.864460e-04 0.0012113670 0.008461027
[2,] 6.001807e-05 1.821978e-04 0.0012865790 0.007407950
[3,] 4.757761e-06 9.187776e-05 0.0007258335 0.010594901
[4,] 8.689338e-05 1.235180e-04 0.0007978501 0.010537201
[5,] 5.954211e-05 1.043253e-04 0.0011843108 0.009300701
[6,] 7.975718e-05 1.922994e-04 0.0011772609 0.010531427
[7,] 1.620214e-05 3.297270e-05 0.0013240382 0.008823996
[8,] 1.105092e-04 6.686237e-05 0.0009338264 0.010049564
[9,] 9.076981e-05 2.494578e-05 0.0006169153 0.007047120
[10,] 5.856918e-05 4.356496e-07 0.0006351191 0.006925511
[11,] 7.000990e-05 1.478814e-04 0.0009264861 0.007761462
[12,] 1.084644e-04 6.333209e-05 0.0010499369 0.007490996
[13,] 6.880415e-05 4.615845e-05 0.0012736670 0.004716146
[14,] 1.265150e-04 4.494923e-05 0.0014221909 0.005422473
[15,] 9.711415e-05 1.056712e-04 0.0015235072 0.005188185
[16,] 1.673792e-04 4.876073e-05 0.0011894826 0.003998747
1487.77010473053 1493.84152373448 1504.49027240276 1512.40640686273
[1,] 2.371239e-04 0.0002838630 2.238168e-04 7.746680e-05
[2,] 2.219024e-04 0.0002455179 2.287891e-04 1.848927e-05
[3,] 2.112691e-04 0.0002094368 2.074949e-04 6.891428e-05
[4,] 9.904719e-05 0.0001666381 1.305881e-04 7.281642e-07
[5,] 1.490856e-04 0.0001894199 2.099925e-04 5.768116e-05
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[8,] 1.779637e-04 0.0001927281 1.553758e-04 6.110146e-05
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[12,] 1.603390e-04 0.0001497066 1.377049e-04 5.754815e-05
[13,] 1.708205e-04 0.0002542594 9.352257e-05 1.801701e-04
[14,] 1.328461e-04 0.0001580887 1.356449e-04 1.076168e-04
[15,] 8.427845e-05 0.0001753045 1.749011e-04 1.337126e-04
[16,] 4.708411e-05 0.0001563716 1.342776e-04 1.220053e-04
1519.53493460045 1528.88120749481 1537.30459790956 1545.95474401565
[1,] 0.0023247099 1.464844e-04 0.0013317701 0.001088890
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[3,] 0.0015874235 2.783179e-04 0.0007627683 0.001135513
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[8,] 0.0016550534 2.449290e-04 0.0006241286 0.002514902
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[10,] 0.0008638777 2.028910e-05 0.0004530070 0.001361992
[11,] 0.0013846872 8.040810e-05 0.0007094614 0.002522900
[12,] 0.0013663469 1.088971e-04 0.0007461704 0.002483034
[13,] 0.0006482916 5.423514e-05 0.0002436958 0.002873641
[14,] 0.0007252242 9.319282e-05 0.0003297046 0.003378113
[15,] 0.0010856287 3.294827e-05 0.0004387025 0.003733621
[16,] 0.0008389731 9.918605e-05 0.0003050284 0.002900152
1562.15223767696 1566.96256995718 1574.18038899719 1585.5798961985
[1,] 4.177747e-04 1.163875e-04 1.878389e-04 1.277970e-04
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[3,] 3.643641e-04 1.706735e-04 9.614087e-05 1.558747e-04
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[6,] 2.190161e-04 1.914229e-04 1.525258e-04 7.047152e-05
[7,] 2.417815e-04 8.899729e-05 1.178627e-04 1.070699e-04
[8,] 1.811082e-04 1.686780e-04 6.417244e-05 1.679707e-04
[9,] 1.325986e-04 1.459315e-04 2.950359e-05 1.747439e-04
[10,] 1.483951e-04 1.822494e-04 1.008369e-04 1.614197e-04
[11,] 1.756068e-04 1.038726e-04 1.382177e-04 1.368217e-04
[12,] 2.453871e-04 1.204247e-04 5.798623e-05 1.661826e-04
[13,] 8.060695e-05 1.591074e-04 4.823004e-05 2.675839e-04
[14,] 9.394031e-05 2.513815e-04 1.108299e-04 2.748696e-04
[15,] 1.978406e-04 1.628090e-04 1.004009e-04 2.580367e-04
[16,] 1.887300e-04 2.422654e-04 5.946040e-05 2.195691e-04
1599.4709032069 1616.98641052309 1627.86862331138 1631.42720633909
[1,] 0.0002075174 0.005476336 3.324490e-06 1.811559e-04
[2,] 0.0002129943 0.005644148 0.000000e+00 2.034339e-04
[3,] 0.0001731297 0.004199524 1.516394e-05 2.488323e-04
[4,] 0.0001793806 0.004388675 1.705546e-06 2.050843e-04
[5,] 0.0001949152 0.003523368 3.808115e-07 1.903052e-04
[6,] 0.0002454233 0.003410956 1.886649e-05 1.630722e-04
[7,] 0.0002554639 0.004317569 1.922867e-05 9.322377e-05
[8,] 0.0001973488 0.003458656 3.312356e-05 1.031073e-04
[9,] 0.0001603952 0.002928532 5.418382e-05 2.010242e-04
[10,] 0.0001122039 0.002716467 1.975453e-05 1.941621e-04
[11,] 0.0001868903 0.003924134 1.134925e-04 1.236056e-04
[12,] 0.0002478587 0.004051122 1.711321e-04 7.983303e-05
[13,] 0.0002155125 0.001548793 3.359569e-04 1.706744e-04
[14,] 0.0001663413 0.001769327 4.703515e-04 2.644428e-04
[15,] 0.0003030426 0.003745185 2.330718e-04 5.230724e-05
[16,] 0.0002504478 0.002867106 2.949215e-04 9.321231e-05
1639.45929690419 1651.11276416591 1658.53077341886 1670.2102001068
[1,] 0.0006125030 6.276364e-05 0.0002696999 4.060154e-04
[2,] 0.0006314174 4.567694e-05 0.0002340071 4.946653e-04
[3,] 0.0003468659 9.387738e-05 0.0002790883 2.783777e-04
[4,] 0.0002972133 1.057854e-04 0.0002768430 3.493454e-04
[5,] 0.0003716145 2.175164e-04 0.0001997197 2.989515e-04
[6,] 0.0003318174 1.448724e-04 0.0001749424 2.541720e-04
[7,] 0.0003474338 1.312140e-04 0.0002493504 2.818380e-04
[8,] 0.0002985235 2.577586e-05 0.0002526711 2.185110e-04
[9,] 0.0002057104 5.936504e-05 0.0002456281 1.772713e-04
[10,] 0.0001849806 6.972620e-05 0.0002643977 1.346832e-04
[11,] 0.0001951465 2.224576e-04 0.0002487231 2.492554e-04
[12,] 0.0002603250 2.094187e-04 0.0002354715 2.344824e-04
[13,] 0.0003021292 1.976703e-07 0.0004199780 2.829821e-05
[14,] 0.0003198325 1.118757e-04 0.0004549577 4.732269e-05
[15,] 0.0002371824 1.532919e-04 0.0004829902 2.549335e-04
[16,] 0.0002110238 2.893491e-05 0.0003394266 1.349240e-04
1678.10908094129 1689.96318821517 1700.25834038674 1707.72235295281
[1,] 1.392078e-04 1.006360e-04 1.276189e-04 8.591187e-05
[2,] 7.450980e-05 9.032117e-05 1.033076e-04 9.728830e-05
[3,] 1.459526e-04 1.024688e-04 8.253903e-05 5.929062e-05
[4,] 8.176950e-05 1.255052e-04 1.273023e-04 1.057481e-04
[5,] 1.028120e-04 1.264754e-04 1.843043e-04 1.059355e-04
[6,] 1.056537e-04 1.317094e-04 2.186670e-04 1.302167e-04
[7,] 1.006836e-04 7.025087e-05 1.101329e-04 4.451827e-05
[8,] 8.778079e-05 1.147581e-04 1.344569e-04 1.262245e-04
[9,] 1.050422e-04 1.152304e-04 8.282808e-05 1.112067e-04
[10,] 9.129999e-05 1.119457e-04 4.080845e-07 8.572826e-05
[11,] 1.090062e-04 1.499951e-04 4.921461e-05 4.533030e-05
[12,] 8.496837e-05 1.174908e-04 4.986559e-05 5.486022e-05
[13,] 1.463404e-04 2.998688e-04 3.972240e-05 1.126490e-04
[14,] 1.418550e-04 3.573476e-04 2.033825e-05 1.379743e-04
[15,] 2.055325e-04 2.729138e-04 4.147137e-05 6.171116e-05
[16,] 2.382952e-04 2.152743e-04 9.248477e-05 1.383185e-04
1710.94797400131 1721.49989354865 1726.37628285335 1732.69152290052
[1,] 9.312055e-05 1.637668e-04 6.176865e-05 1.561488e-04
[2,] 4.077644e-05 1.596940e-04 8.840894e-05 1.059169e-04
[3,] 2.742494e-05 1.063736e-04 1.007112e-04 1.221481e-04
[4,] 1.328357e-04 1.562474e-04 8.587984e-05 1.374459e-04
[5,] 5.340509e-05 3.202284e-04 1.077884e-04 8.113065e-05
[6,] 1.359284e-04 3.486198e-04 1.356449e-04 7.388044e-05
[7,] 6.140057e-05 1.364646e-04 1.315979e-04 8.011449e-05
[8,] 7.849337e-05 6.590417e-05 8.856508e-05 1.222035e-04
[9,] 9.761783e-05 2.031660e-04 9.123809e-05 1.533156e-04
[10,] 1.505616e-04 1.769646e-04 6.050925e-05 6.770983e-05
[11,] 4.334502e-05 2.402103e-04 1.020451e-04 5.163430e-05
[12,] 8.533540e-05 2.627318e-04 1.179226e-04 8.885744e-05
[13,] 7.444306e-05 1.054560e-04 4.210347e-05 1.360234e-04
[14,] 7.235717e-05 3.683337e-05 5.140250e-05 2.818846e-05
[15,] 1.047096e-04 3.839765e-05 1.006270e-04 1.438118e-04
[16,] 1.200142e-04 3.766927e-05 5.661108e-05 9.696926e-05
1740.88745076166 1751.59839250932 1762.4603329154 1780.12418478236
[1,] 0.0002087322 1.281397e-04 1.549018e-04 1.938956e-04
[2,] 0.0001663852 1.288924e-04 1.744776e-04 2.132682e-04
[3,] 0.0001417929 1.298468e-04 1.208877e-04 1.312395e-04
[4,] 0.0001323941 1.484237e-04 1.211409e-04 1.180724e-04
[5,] 0.0001241061 1.217717e-04 1.070270e-04 1.066622e-04
[6,] 0.0001201607 1.345719e-04 4.500351e-05 1.940551e-05
[7,] 0.0001263754 1.420238e-04 7.766099e-05 1.226814e-04
[8,] 0.0001141785 1.933445e-04 8.676911e-05 1.342016e-04
[9,] 0.0003854311 1.652074e-04 1.045134e-04 1.880547e-04
[10,] 0.0003490557 1.578452e-04 1.121439e-04 2.090918e-04
[11,] 0.0009738592 4.132653e-05 1.403284e-04 1.582228e-04
[12,] 0.0010156009 6.420694e-05 1.149541e-04 1.485620e-04
[13,] 0.0009469649 1.520576e-04 1.605917e-04 5.715648e-04
[14,] 0.0010121116 1.298161e-04 1.380853e-04 6.154972e-04
[15,] 0.0009792506 1.237609e-04 1.294907e-04 5.209629e-04
[16,] 0.0009582090 1.535423e-04 1.515239e-04 4.601893e-04
1786.53450863593 1793.58470130077 1798.83732503704 1803.22717723354
[1,] 6.492143e-05 1.578445e-05 2.510884e-05 1.725227e-05
[2,] 1.181610e-04 9.431809e-06 3.730785e-05 7.574469e-05
[3,] 9.107904e-05 3.330469e-05 8.584707e-05 2.661161e-05
[4,] 1.088618e-04 8.655549e-05 6.463441e-05 8.982223e-05
[5,] 1.035328e-04 9.340808e-06 1.112093e-04 4.532284e-05
[6,] 8.209426e-05 3.610568e-05 5.438385e-05 9.420791e-05
[7,] 3.707573e-05 2.280092e-05 8.612519e-05 1.900420e-05
[8,] 8.963526e-05 2.300703e-05 1.122045e-04 3.969368e-05
[9,] 1.103785e-04 7.156057e-05 6.853478e-06 4.561515e-05
[10,] 4.215249e-05 9.736440e-05 1.101532e-04 4.081336e-05
[11,] 1.431532e-04 8.510243e-05 3.006308e-05 3.189152e-05
[12,] 9.821197e-05 5.405068e-05 3.437510e-06 4.831575e-05
[13,] 1.093650e-04 1.105278e-04 1.004690e-04 9.011655e-05
[14,] 1.375540e-04 1.217353e-04 3.756828e-05 2.289514e-05
[15,] 1.521713e-04 5.260124e-05 7.439363e-05 1.033504e-04
[16,] 6.565214e-05 1.111209e-04 9.552264e-05 5.108463e-05
1813.21289406907 1824.25933192842 1827.36577832849 1829.60110408585
[1,] 9.756783e-05 4.878558e-05 6.218717e-05 1.141694e-04
[2,] 1.504232e-04 7.918038e-05 9.361472e-05 8.275335e-05
[3,] 1.248849e-04 7.480714e-05 9.776014e-05 1.093575e-04
[4,] 1.675260e-04 1.487339e-04 1.201529e-04 3.927532e-05
[5,] 2.340637e-04 6.162308e-05 1.155250e-04 1.409908e-04
[6,] 2.320953e-04 9.776963e-05 7.141624e-05 1.269255e-04
[7,] 1.592861e-04 7.702751e-05 1.120494e-04 8.544709e-05
[8,] 1.409674e-04 8.184815e-05 5.232772e-05 8.033322e-05
[9,] 1.966938e-04 9.480721e-05 1.519405e-04 9.543852e-05
[10,] 1.639608e-04 9.405461e-05 4.662345e-05 5.476241e-05
[11,] 3.086697e-04 6.936817e-05 9.719406e-05 7.858765e-05
[12,] 2.534982e-04 1.144396e-04 1.167052e-04 7.368329e-05
[13,] 1.003292e-04 1.092223e-04 5.176219e-05 1.833673e-04
[14,] 1.867939e-04 8.128420e-05 4.246353e-05 8.539360e-05
[15,] 2.846016e-04 8.638425e-05 3.664104e-05 1.341687e-04
[16,] 1.543011e-04 1.209850e-04 1.641887e-04 1.100026e-04
1838.02081192954 1847.41616881623 1854.42798468778 1866.03867377166
[1,] 8.120678e-05 1.121368e-04 1.438886e-04 1.785663e-04
[2,] 1.895542e-05 1.014995e-04 1.003379e-04 2.485721e-04
[3,] 3.151914e-05 1.253651e-04 1.219050e-04 1.330701e-04
[4,] 8.322154e-05 1.234086e-04 1.315354e-04 1.559811e-04
[5,] 1.003978e-05 1.366186e-04 1.005518e-04 7.439949e-05
[6,] 5.399471e-07 1.509272e-04 6.607081e-05 1.094698e-04
[7,] 3.732298e-05 1.632835e-04 1.173595e-04 9.188587e-05
[8,] 1.968113e-05 1.457679e-04 1.414629e-04 8.032486e-05
[9,] 1.071058e-04 1.614115e-04 1.402300e-04 2.148734e-04
[10,] 2.460652e-05 1.161125e-04 1.346448e-04 1.446636e-04
[11,] 4.907638e-05 1.314751e-04 1.136344e-04 1.105461e-04
[12,] 2.970032e-05 8.747631e-05 1.469664e-04 1.655267e-04
[13,] 1.028000e-04 2.490224e-05 2.030691e-04 1.215495e-03
[14,] 5.580701e-05 1.064028e-04 1.673847e-04 1.400614e-03
[15,] 1.015494e-04 1.411628e-04 1.846093e-04 1.188124e-03
[16,] 9.594626e-05 9.554840e-05 1.042661e-04 1.086262e-03
1871.91763556466 1885.78540717647 1896.94770218653 1913.31371764638
[1,] 1.267063e-04 0.0002665196 1.551191e-04 3.360471e-05
[2,] 9.864471e-05 0.0002413051 2.496363e-04 8.151347e-05
[3,] 1.558769e-04 0.0002489687 1.717940e-04 6.751770e-05
[4,] 1.117710e-04 0.0002735786 2.146829e-04 1.040257e-04
[5,] 1.315141e-04 0.0002922930 5.712434e-05 3.828609e-05
[6,] 8.625261e-05 0.0004102350 8.141357e-05 4.220773e-05
[7,] 1.130486e-04 0.0003154737 1.635667e-04 1.079613e-05
[8,] 1.379378e-04 0.0002794961 1.608019e-04 4.361037e-05
[9,] 1.448979e-04 0.0003239945 2.579142e-04 6.472126e-05
[10,] 1.201101e-04 0.0003684737 2.149053e-04 9.189398e-05
[11,] 1.304353e-04 0.0003283980 4.472305e-04 8.905937e-05
[12,] 1.397866e-04 0.0003396399 4.643332e-04 9.312076e-05
[13,] 2.065523e-04 0.0001363005 6.812422e-04 4.703205e-06
[14,] 3.294648e-04 0.0001916278 7.879597e-04 1.121222e-05
[15,] 1.636076e-04 0.0001552622 7.458187e-04 3.993349e-05
[16,] 2.064475e-04 0.0001894109 7.569052e-04 2.865413e-05
1917.00546281844 1927.81570236503 1944.86406788556 1955.14173099136
[1,] 9.513152e-05 0.0001345358 0.0002394944 8.391304e-05
[2,] 6.135019e-05 0.0001179003 0.0002470171 7.787806e-05
[3,] 9.258103e-05 0.0001499936 0.0005039498 1.233698e-04
[4,] 9.321533e-05 0.0001513254 0.0006390205 1.143304e-04
[5,] 4.401076e-05 0.0001039729 0.0006730760 3.540551e-05
[6,] 5.212582e-05 0.0001227752 0.0006979968 1.956333e-05
[7,] 8.255531e-05 0.0001321905 0.0002511830 6.400888e-05
[8,] 1.126035e-04 0.0001919540 0.0002621301 3.588926e-05
[9,] 6.605822e-05 0.0001289126 0.0002548832 4.407845e-05
[10,] 1.164548e-04 0.0001336565 0.0002312344 1.668967e-04
[11,] 9.404751e-05 0.0001404503 0.0006690600 8.439041e-05
[12,] 9.826662e-05 0.0001434357 0.0007847590 1.170998e-04
[13,] 1.259273e-04 0.0002472886 0.0023130676 4.090075e-06
[14,] 4.623779e-05 0.0001918960 0.0024978588 5.439147e-05
[15,] 4.688196e-05 0.0002232550 0.0031044485 4.647506e-05
[16,] 8.101384e-05 0.0002194260 0.0026240421 1.021399e-05
1968.21217364147 1984.94817425438 1994.92122529695 2000.34417275119
[1,] 1.499311e-04 8.781365e-05 9.918636e-05 1.115264e-04
[2,] 1.614689e-04 1.157809e-04 1.153719e-04 1.541173e-04
[3,] 2.028918e-04 1.501982e-04 1.554737e-04 1.025739e-04
[4,] 2.139683e-04 1.598104e-04 1.206852e-04 9.853364e-05
[5,] 1.727315e-04 1.342821e-04 1.254199e-04 1.783185e-04
[6,] 1.333272e-04 1.371565e-04 1.041697e-04 1.661975e-04
[7,] 8.961914e-05 8.759468e-05 1.103399e-04 1.357034e-04
[8,] 1.281254e-04 8.700036e-05 1.505479e-04 1.167606e-04
[9,] 1.431568e-04 1.045759e-04 5.999534e-05 1.299048e-04
[10,] 1.930887e-04 1.263568e-04 5.550803e-05 1.124954e-04
[11,] 1.304107e-04 8.515720e-05 1.791050e-04 1.889493e-04
[12,] 1.191380e-04 1.217329e-04 9.260890e-05 2.241031e-04
[13,] 7.884494e-05 3.127253e-05 1.455721e-04 1.333831e-04
[14,] 4.883531e-05 9.909674e-05 8.947932e-05 1.740906e-04
[15,] 1.468028e-04 5.495621e-05 1.110110e-04 2.096444e-04
[16,] 1.441825e-04 9.896751e-05 4.826561e-05 1.854423e-04
2010.4999933461 2019.59918169402 2023.59679714659 2039.10593929578
[1,] 9.146884e-05 1.391288e-04 5.111721e-05 5.392045e-05
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[6,] 0.0004700785 4.468140e-05 1.170420e-04 9.338966e-05
[7,] 0.0006216716 1.463152e-04 4.905781e-05 2.399920e-07
[8,] 0.0006453499 8.911636e-05 6.175965e-05 8.555982e-07
[9,] 0.0006313176 8.499829e-05 1.630949e-05 8.779554e-05
[10,] 0.0005730394 6.840490e-05 8.052639e-05 8.212430e-05
[11,] 0.0004540206 2.625553e-05 9.148972e-05 1.190173e-05
[12,] 0.0004561826 8.130549e-05 1.022945e-04 1.596672e-05
[13,] 0.0004857873 1.052311e-04 1.202348e-04 2.745063e-05
[14,] 0.0004713260 9.676022e-05 1.150209e-04 1.107510e-05
[15,] 0.0003897384 5.206998e-05 2.378936e-05 9.722718e-05
[16,] 0.0003829319 1.231729e-04 2.518052e-05 6.999821e-06
8300.23695654572 8360.79232858583 8403.9333077669 8448.37237425014
[1,] 8.033874e-05 9.779571e-05 3.801710e-05 9.482375e-05
[2,] 7.804554e-05 7.978252e-05 1.447839e-05 4.568176e-05
[3,] 1.067296e-04 6.288990e-05 2.324086e-05 8.426738e-05
[4,] 3.464708e-05 3.355977e-05 2.259768e-05 1.694477e-05
[5,] 1.667386e-05 6.733392e-06 7.916531e-05 5.516598e-05
[6,] 8.258282e-05 9.523248e-05 8.495451e-05 5.479485e-06
[7,] 7.724712e-05 1.891310e-05 2.662532e-05 1.824367e-06
[8,] 3.599286e-05 4.963830e-05 2.623290e-05 4.442726e-05
[9,] 3.083501e-05 8.575395e-05 4.495703e-05 3.011046e-05
[10,] 1.195120e-05 1.462869e-04 6.002473e-05 4.254249e-05
[11,] 2.858284e-06 4.010178e-05 1.088138e-05 1.643384e-05
[12,] 2.965021e-05 5.816686e-05 4.159309e-05 3.236004e-05
[13,] 1.313065e-04 6.403867e-05 2.922466e-05 9.658956e-05
[14,] 9.412851e-05 5.434407e-05 5.778597e-05 9.726899e-05
[15,] 2.455199e-05 2.896057e-05 9.719633e-05 4.084361e-05
[16,] 4.604154e-05 9.946906e-05 1.282160e-04 1.003625e-04
8525.12160724765 8566.85309035483 8604.51267786125 8655.97673017059
[1,] 1.137258e-04 3.842261e-05 1.417324e-04 8.362850e-05
[2,] 1.193534e-04 3.037121e-05 1.832415e-04 5.387693e-05
[3,] 9.957327e-05 3.033096e-05 1.289253e-04 2.351883e-05
[4,] 8.539218e-05 4.648777e-05 7.880319e-05 1.160621e-06
[5,] 7.921968e-05 9.121955e-05 1.781828e-04 1.808934e-05
[6,] 3.422208e-06 6.193236e-05 1.226379e-04 7.989106e-05
[7,] 6.194545e-06 9.012279e-05 8.832638e-05 8.907244e-05
[8,] 3.745211e-05 1.205876e-04 1.204487e-04 5.325141e-05
[9,] 9.627386e-05 8.913427e-05 2.153593e-04 1.007385e-04
[10,] 5.736500e-05 1.377951e-05 2.288331e-04 2.406799e-05
[11,] 4.589699e-06 4.598377e-05 5.674016e-04 1.320198e-04
[12,] 4.121858e-07 1.138595e-05 4.766608e-04 7.927219e-05
[13,] 1.584211e-05 4.759633e-05 3.776711e-04 1.743512e-05
[14,] 3.384465e-05 4.946307e-05 4.770375e-04 4.025981e-05
[15,] 9.560251e-05 6.296777e-05 5.630471e-04 2.703972e-05
[16,] 1.036456e-04 3.218584e-05 5.916387e-04 1.130047e-04
8665.41662049227 8678.01039477937 8757.3549358545 8767.92932641375
[1,] 7.753542e-05 1.359941e-04 3.093924e-05 1.228501e-05
[2,] 6.429774e-05 1.267077e-04 3.076574e-05 3.690764e-05
[3,] 9.235710e-05 6.314258e-05 7.027310e-05 4.349036e-05
[4,] 5.052533e-05 3.397891e-05 9.260638e-05 1.191981e-04
[5,] 6.485745e-05 2.859456e-05 2.833230e-05 1.018685e-04
[6,] 6.635245e-05 7.994193e-05 8.955487e-05 3.960390e-05
[7,] 8.778663e-05 1.434037e-04 4.301968e-05 2.342260e-05
[8,] 1.677209e-05 1.613862e-04 9.598605e-05 1.003428e-04
[9,] 6.509818e-05 1.381485e-04 7.019186e-05 4.784341e-05
[10,] 7.340937e-05 1.647202e-04 4.648047e-05 9.903721e-05
[11,] 2.708895e-05 3.671651e-05 2.519587e-05 9.329727e-05
[12,] 8.414234e-05 4.031442e-05 1.916711e-05 3.791910e-05
[13,] 1.041738e-04 1.371049e-04 1.011810e-04 5.453843e-05
[14,] 5.415741e-05 1.762715e-04 5.062142e-05 1.803241e-05
[15,] 9.146426e-05 4.552085e-05 9.397622e-05 9.077471e-06
[16,] 8.701893e-05 1.058691e-04 7.458948e-05 2.322523e-05
8814.77533912157 8866.06100847097 8935.06794034845 8989.43515078892
[1,] 1.734513e-05 1.595436e-04 0.0002560773 9.144292e-05
[2,] 1.175136e-05 1.253998e-04 0.0002337337 7.410130e-05
[3,] 1.368763e-09 7.783817e-05 0.0002598821 6.947640e-05
[4,] 1.035625e-04 1.058826e-04 0.0002927928 1.069207e-04
[5,] 2.694877e-05 1.342759e-04 0.0002515544 2.716680e-05
[6,] 8.488831e-05 1.242758e-04 0.0002451851 5.481525e-05
[7,] 4.911166e-05 1.896825e-04 0.0003156515 3.717536e-05
[8,] 5.268388e-05 2.057835e-04 0.0003444933 9.911025e-05
[9,] 2.178417e-05 1.894813e-04 0.0003329390 2.883577e-05
[10,] 1.999542e-05 2.035653e-04 0.0003269643 2.504850e-05
[11,] 1.325219e-05 5.099132e-05 0.0005190050 5.565199e-05
[12,] 1.198911e-05 6.111196e-05 0.0004448035 9.973302e-05
[13,] 5.169495e-05 1.934184e-04 0.0012470229 2.382435e-04
[14,] 6.717232e-05 1.807355e-04 0.0010917186 2.074109e-04
[15,] 9.781502e-05 4.941777e-05 0.0008820036 1.942623e-04
[16,] 1.111676e-04 5.417759e-05 0.0010445366 2.526371e-04
9062.85844596155 9069.00478695729 9134.68794526052 9175.73637670355
[1,] 1.814524e-04 1.414284e-04 8.224114e-05 3.535614e-05
[2,] 1.624278e-04 7.771146e-05 8.122614e-05 7.922658e-05
[3,] 1.157078e-04 3.206264e-05 9.150583e-05 1.007372e-04
[4,] 8.325051e-05 8.264137e-05 5.238733e-05 1.181386e-04
[5,] 1.209568e-04 8.855473e-05 1.469121e-04 3.931997e-05
[6,] 1.036140e-04 6.356548e-05 1.189324e-04 8.862494e-05
[7,] 2.601210e-04 2.821402e-04 5.674613e-05 8.895665e-05
[8,] 2.482874e-04 1.984213e-04 7.986869e-05 5.183579e-05
[9,] 2.631114e-04 1.027479e-04 8.431979e-05 1.275705e-04
[10,] 2.926239e-04 1.544128e-04 1.159575e-04 1.064482e-04
[11,] 1.298913e-04 6.289610e-05 1.461067e-04 4.427329e-05
[12,] 1.193041e-04 6.732293e-05 1.788009e-04 3.682427e-05
[13,] 1.541560e-04 2.114034e-04 1.985056e-04 1.050420e-04
[14,] 1.401536e-04 1.849431e-04 1.485212e-04 1.302402e-04
[15,] 1.046966e-04 7.026807e-05 1.847851e-04 2.647688e-05
[16,] 7.900325e-05 4.447013e-05 2.005776e-04 3.947075e-05
9291.15773142448 9345.1564865995 9424.85875994165 9482.82463667587
[1,] 0.001280714 0.0002202338 6.074382e-05 8.106983e-05
[2,] 0.001273002 0.0002749855 1.277287e-04 4.383811e-05
[3,] 0.001720177 0.0002909552 1.547122e-04 1.140602e-04
[4,] 0.001716018 0.0003424873 1.204489e-04 1.555887e-05
[5,] 0.001570641 0.0002846512 1.375294e-04 8.206590e-05
[6,] 0.001553855 0.0003122606 1.596793e-04 3.281581e-05
[7,] 0.001698991 0.0002999962 8.446072e-05 6.286047e-05
[8,] 0.001676291 0.0002981964 9.627002e-05 5.903656e-05
[9,] 0.001900896 0.0003211738 1.352188e-04 1.169340e-04
[10,] 0.001928834 0.0002520920 1.549451e-04 3.902743e-05
[11,] 0.001392380 0.0002101953 2.025382e-04 3.063965e-05
[12,] 0.001393374 0.0002095867 1.723245e-04 9.168378e-05
[13,] 0.001817146 0.0002748477 1.898376e-04 1.070335e-04
[14,] 0.001724297 0.0002227287 1.209929e-04 9.352800e-05
[15,] 0.001205344 0.0001959438 1.805530e-04 4.563477e-05
[16,] 0.001205050 0.0002550883 2.314664e-04 5.267743e-05
9520.0958998593 9591.52804513401 9627.02624105309 9680.91371813213
[1,] 8.684708e-05 8.066605e-05 1.932930e-05 1.911105e-05
[2,] 4.927890e-05 3.190534e-05 1.464944e-05 2.024309e-05
[3,] 8.420204e-05 4.173678e-05 5.757345e-06 3.852158e-05
[4,] 2.838171e-05 8.051185e-05 2.900602e-06 1.013909e-04
[5,] 2.828673e-05 2.487295e-07 3.670969e-05 7.754962e-05
[6,] 5.028826e-05 8.109296e-05 1.654005e-05 8.071084e-05
[7,] 3.074848e-05 8.243928e-05 8.740877e-05 3.250402e-05
[8,] 6.131937e-05 8.927497e-05 7.999676e-05 8.055743e-05
[9,] 2.087587e-05 9.228381e-05 9.110183e-05 1.700602e-05
[10,] 9.164656e-05 5.085426e-05 9.491501e-05 3.892972e-05
[11,] 3.894947e-05 3.139731e-05 4.256182e-05 7.263782e-06
[12,] 3.505194e-05 5.075846e-05 8.240666e-05 5.622369e-05
[13,] 6.410080e-05 1.063271e-04 3.851150e-05 9.980570e-06
[14,] 1.836862e-05 5.900693e-05 4.094089e-05 6.395349e-05
[15,] 4.662595e-05 4.739651e-05 1.009152e-04 4.783011e-05
[16,] 9.788525e-05 1.812729e-06 5.832966e-05 3.529670e-05
9716.88151636139 9763.32695995444 9778.49794540648 9821.33144286695
[1,] 8.241886e-05 8.221088e-05 3.815164e-05 5.111016e-05
[2,] 8.573315e-05 0.000000e+00 8.366774e-05 8.127069e-05
[3,] 1.107679e-04 4.875462e-05 4.781607e-05 4.783303e-05
[4,] 4.273409e-05 1.007155e-04 3.550231e-05 7.948118e-05
[5,] 6.558358e-05 2.353438e-05 4.250561e-05 5.598081e-05
[6,] 8.876221e-05 8.810046e-05 1.158909e-04 2.826572e-05
[7,] 7.700724e-05 8.741472e-05 3.859577e-05 8.622340e-05
[8,] 4.023760e-05 4.720982e-05 1.171574e-05 8.571037e-05
[9,] 3.268271e-05 4.303301e-05 9.117880e-05 2.992569e-05
[10,] 6.082295e-05 9.086325e-05 8.894737e-05 2.687786e-05
[11,] 8.866494e-05 3.504367e-05 2.420127e-05 9.314524e-06
[12,] 8.970062e-05 5.449691e-05 5.438122e-05 7.521919e-07
[13,] 1.489362e-04 2.609733e-06 5.382143e-05 3.940519e-06
[14,] 9.596334e-05 1.131280e-05 4.491978e-05 4.592572e-05
[15,] 7.062143e-05 1.106149e-05 3.630255e-05 7.172462e-05
[16,] 9.578853e-05 4.052634e-05 7.055970e-05 9.788043e-05
9862.10889558263 9881.38500681933 9928.20598274695 9955.81436772147
[1,] 6.522456e-05 8.407020e-05 7.722769e-05 8.954388e-05
[2,] 8.639574e-05 3.158370e-05 7.399899e-06 4.971387e-05
[3,] 8.498395e-05 1.885654e-05 8.369060e-05 1.141453e-04
[4,] 9.939353e-05 3.952850e-05 9.112190e-05 9.172190e-05
[5,] 3.159189e-05 1.487561e-05 1.469206e-05 8.262629e-05
[6,] 9.648680e-05 8.258236e-05 7.199091e-05 1.004320e-04
[7,] 8.965699e-05 2.606077e-05 5.346342e-05 1.165456e-04
[8,] 8.211547e-06 1.279400e-05 4.103961e-05 5.168985e-05
[9,] 4.249961e-05 2.177044e-05 9.457338e-05 5.080493e-05
[10,] 2.239665e-05 5.807923e-05 6.272456e-05 2.470524e-05
[11,] 8.241097e-05 3.614287e-05 2.024048e-05 1.496811e-05
[12,] 3.895223e-05 3.144318e-05 1.995491e-05 9.014947e-05
[13,] 1.116747e-04 1.038458e-04 1.204455e-04 2.730718e-05
[14,] 2.703343e-06 4.072027e-05 1.549195e-05 7.730739e-05
[15,] 2.062250e-05 1.025027e-04 9.833403e-05 1.065755e-04
[16,] 3.392761e-05 5.420147e-05 1.241513e-05 1.073195e-04
9978.11979499518
[1,] 2.688357e-05
[2,] 2.209309e-05
[3,] 3.472585e-05
[4,] 2.403174e-05
[5,] 5.054133e-05
[6,] 1.100106e-04
[7,] 5.537434e-05
[8,] 6.650622e-06
[9,] 5.250587e-05
[10,] 8.279314e-05
[11,] 7.252752e-05
[12,] 2.475382e-05
[13,] 1.120467e-04
[14,] 9.427786e-05
[15,] 4.507615e-05
[16,] 6.514152e-05
attr(,"mass")
[1] 1004.317 1011.418 1020.643 1029.548 1041.149 1046.766 1057.943 1065.435
[9] 1077.515 1083.768 1102.588 1113.699 1127.510 1131.445 1139.398 1151.039
[17] 1157.648 1169.253 1175.802 1181.968 1187.903 1191.467 1199.712 1206.772
[25] 1220.612 1224.675 1227.636 1229.957 1237.366 1244.557 1254.943 1263.615
[33] 1273.449 1279.548 1291.893 1299.097 1309.301 1317.848 1329.185 1334.501
[41] 1350.973 1363.753 1371.520 1378.894 1381.537 1389.914 1396.360 1400.807
[49] 1404.485 1413.922 1419.914 1426.415 1431.954 1435.107 1449.720 1466.005
[57] 1487.770 1493.842 1504.490 1512.406 1519.535 1528.881 1537.305 1545.955
[65] 1562.152 1566.963 1574.180 1585.580 1599.471 1616.986 1627.869 1631.427
[73] 1639.459 1651.113 1658.531 1670.210 1678.109 1689.963 1700.258 1707.722
[81] 1710.948 1721.500 1726.376 1732.692 1740.887 1751.598 1762.460 1780.124
[89] 1786.535 1793.585 1798.837 1803.227 1813.213 1824.259 1827.366 1829.601
[97] 1838.021 1847.416 1854.428 1866.039 1871.918 1885.785 1896.948 1913.314
[105] 1917.005 1927.816 1944.864 1955.142 1968.212 1984.948 1994.921 2000.344
[113] 2010.500 2019.599 2023.597 2039.106 2045.695 2067.700 2072.065 2081.656
[121] 2092.549 2105.193 2113.759 2134.779 2142.395 2153.384 2161.054 2164.564
[129] 2178.598 2190.376 2194.922 2209.945 2225.887 2231.510 2243.276 2246.906
[137] 2251.533 2265.175 2279.820 2294.368 2307.258 2318.795 2325.909 2337.033
[145] 2347.686 2353.952 2368.881 2379.312 2392.606 2403.030 2409.236 2423.436
[153] 2428.832 2448.992 2467.088 2472.423 2482.192 2495.069 2509.722 2521.995
[161] 2535.642 2544.892 2553.928 2561.095 2584.163 2588.305 2604.422 2611.028
[169] 2616.707 2623.023 2632.678 2643.094 2660.099 2672.778 2681.895 2687.230
[177] 2698.464 2708.185 2713.260 2723.513 2730.799 2739.433 2754.227 2769.271
[185] 2789.955 2808.498 2819.685 2830.095 2845.184 2862.330 2887.845 2902.135
[193] 2915.240 2932.355 2952.511 2972.031 2978.481 2990.580 3001.472 3016.837
[201] 3045.081 3059.823 3076.245 3087.348 3096.035 3104.668 3117.848 3142.924
[209] 3158.677 3174.890 3191.691 3207.758 3217.129 3231.303 3240.742 3262.770
[217] 3279.329 3293.909 3302.790 3315.938 3325.283 3334.893 3365.417 3377.510
[225] 3408.515 3414.073 3419.994 3434.326 3448.050 3465.780 3486.267 3506.032
[233] 3510.307 3524.489 3540.163 3568.218 3585.225 3596.215 3606.117 3625.612
[241] 3635.111 3657.607 3676.239 3681.998 3701.147 3719.806 3734.647 3764.910
[249] 3782.682 3795.660 3816.913 3861.577 3874.351 3882.894 3901.955 3909.581
[257] 3918.923 3934.249 3952.639 3961.742 3992.659 4018.815 4036.936 4053.890
[265] 4064.964 4071.510 4091.219 4110.290 4122.741 4142.827 4148.384 4166.734
[273] 4172.064 4192.605 4209.975 4235.206 4249.487 4266.249 4301.672 4338.144
[281] 4354.758 4362.900 4382.243 4397.053 4415.758 4431.190 4466.957 4494.914
[289] 4530.456 4567.286 4585.491 4614.802 4644.358 4662.529 4671.428 4696.628
[297] 4710.995 4716.438 4738.345 4757.773 4787.358 4816.414 4820.055 4857.909
[305] 4889.123 4914.700 4920.570 4963.964 5003.839 5024.202 5045.565 5131.326
[313] 5160.157 5204.684 5248.447 5264.658 5292.836 5320.175 5336.912 5376.684
[321] 5390.746 5438.558 5461.635 5481.727 5507.239 5522.850 5550.761 5592.509
[329] 5664.615 5687.078 5754.985 5805.897 5860.963 5887.746 5904.769 5944.466
[337] 5958.666 6005.629 6049.333 6074.880 6090.263 6130.692 6175.185 6191.783
[345] 6228.274 6270.594 6305.137 6333.115 6373.672 6379.663 6433.208 6488.349
[353] 6519.969 6561.650 6631.501 6663.896 6670.483 6684.579 6780.271 6806.213
[361] 6832.224 6853.808 6908.657 6962.618 7006.988 7011.725 7149.239 7190.433
[369] 7221.800 7293.721 7314.103 7368.091 7401.256 7436.046 7472.088 7534.933
[377] 7565.873 7638.752 7721.691 7766.777 7807.424 7820.587 7835.652 7865.843
[385] 7908.719 7925.001 8093.068 8132.485 8142.988 8194.464 8217.936 8248.854
[393] 8300.237 8360.792 8403.933 8448.372 8525.122 8566.853 8604.513 8655.977
[401] 8665.417 8678.010 8757.355 8767.929 8814.775 8866.061 8935.068 8989.435
[409] 9062.858 9069.005 9134.688 9175.736 9291.158 9345.156 9424.859 9482.825
[417] 9520.096 9591.528 9627.026 9680.914 9716.882 9763.327 9778.498 9821.331
[425] 9862.109 9881.385 9928.206 9955.814 9978.120
(def r-ms-dims (r->clj (r "dim(fm_ms)")))(def bin-filter-pass?
(every? #(and (:match? %) (< (:max-diff %) 1e-10)) bin-filter-comparison)){:clj-dims [(:n-spectra clj-ms-im) (:n-features clj-ms-im)]
:r-dims (vec r-ms-dims)
:dims-match? (and (= (:n-spectra clj-ms-im) (long (first r-ms-dims)))
(= (:n-features clj-ms-im) (long (second r-ms-dims))))
:bin-filter-pass? bin-filter-pass?}{:clj-dims [16 429],
:r-dims [16 429],
:dims-match? true,
:bin-filter-pass? true}End-to-End Multi-Sample Pipeline
Run the full pipeline independently in Ripple and MALDIquant, then compare. The expected difference: SG smoothing edge effects cause ±1 peak differences, which can propagate to ±1 feature in the final matrix.
(def clj-e2e-spectra
(mapv (fn [i]
(tc/dataset {:mass (r->clj (r (format "mass(fiedler2009subset[[%d]])" (inc i))))
:intensity (r->clj (r (format "intensity(fiedler2009subset[[%d]])" (inc i))))}))
(range 16)))(def clj-e2e-preprocessed
(mapv #(signal/preprocess-spectrum-data %
{:should-sqrt-transform true
:smooth-window 11
:smooth-polynomial 2
:baseline-iterations 25
:baseline-repetitions 1
:should-tic-normalize true
:tic-target 1.0})
clj-e2e-spectra))(def clj-e2e-peak-lists
(mapv (fn [ds]
(let [masses-arr (double-array (:mass ds))
intens-arr (double-array (:intensity ds))
indices (signal/detect-peaks intens-arr {:half-window-size 20 :snr 2})]
{:masses (double-array (map #(aget masses-arr %) indices))
:intensities (double-array (map #(aget intens-arr %) indices))}))
clj-e2e-preprocessed))(def clj-e2e-binned (maldi/bin-peaks-multi clj-e2e-peak-lists {:tolerance 0.002}))(def clj-e2e-filtered (maldi/filter-peaks-multi clj-e2e-binned {:min-frequency 0.25}))(def clj-e2e-im (maldi/intensity-matrix clj-e2e-filtered))Compare dimensions
(def e2e-dims
{:clj [(:n-spectra clj-e2e-im) (:n-features clj-e2e-im)]
:r (mapv long (r->clj (r "dim(fm_ms)")))})Peak count comparison
(def e2e-peak-counts
(mapv (fn [i]
(let [r-count (long (first (r->clj (r (format "length(mass(peaks_ms[[%d]]))" (inc i))))))
clj-count (count (:masses (nth clj-e2e-peak-lists i)))]
{:spectrum (inc i) :r r-count :clj clj-count :diff (- clj-count r-count)}))
(range 16)))(kind/table
{:column-names ["Spectrum" "R Peaks" "Clj Peaks" "Diff"]
:row-vectors (mapv (fn [{:keys [spectrum r clj diff]}]
[spectrum r clj diff])
e2e-peak-counts)})| Spectrum | R Peaks | Clj Peaks | Diff |
|---|---|---|---|
| 1 | 330 | 330 | 0 |
| 2 | 309 | 309 | 0 |
| 3 | 355 | 355 | 0 |
| 4 | 350 | 349 | -1 |
| 5 | 299 | 299 | 0 |
| 6 | 342 | 342 | 0 |
| 7 | 325 | 325 | 0 |
| 8 | 311 | 311 | 0 |
| 9 | 309 | 309 | 0 |
| 10 | 319 | 319 | 0 |
| 11 | 275 | 275 | 0 |
| 12 | 281 | 281 | 0 |
| 13 | 329 | 329 | 0 |
| 14 | 303 | 303 | 0 |
| 15 | 309 | 309 | 0 |
| 16 | 341 | 341 | 0 |
The feature count difference is small — within ±2 features due to SG smoothing boundary effects affecting a handful of borderline peaks.
(def e2e-feature-diff
(Math/abs (- (long (second (:r e2e-dims))) (:n-features clj-e2e-im))))(def e2e-close?
(<= e2e-feature-diff 2)){:clj-features (:n-features clj-e2e-im)
:r-features (long (second (:r e2e-dims)))
:feature-diff e2e-feature-diff
:close? e2e-close?}{:clj-features 428, :r-features 429, :feature-diff 1, :close? true}DRIAMS: Moving Average Comparison
Moving average smoothing on DRIAMS spectra should match MALDIquant exactly (no SG edge effects to worry about).
(defn compare-driams-ma [path]
(let [spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (:intensity spec))
_ (r `(<- s_ma (createMassSpectrum :mass ~masses :intensity ~intensities)))
r-result (r->clj (r "intensity(smoothIntensity(s_ma, method='MovingAverage', halfWindowSize=5))"))
clj-result (signal/moving-average-smooth (:intensity spec) {:half-window-size 5})
max-diff (apply max (map #(Math/abs (- (double %1) (double %2)))
r-result (seq clj-result)))]
{:file (last (clojure.string/split path #"/")) :n-points (count r-result) :max-diff max-diff}))(def driams-ma-results (mapv compare-driams-ma driams-files))(kind/table (tc/dataset driams-ma-results))| file | n-points | max-diff |
|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 20813 | 1.8189894035458565E-12 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 20820 | 1.8189894035458565E-12 |
| 0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz | 20825 | 1.8189894035458565E-12 |
(def driams-ma-all-pass?
(every? #(< (:max-diff %) 1e-10) driams-ma-results))driams-ma-all-pass?trueDRIAMS: TopHat Baseline Comparison
TopHat (morphological opening) on DRIAMS spectra.
(defn compare-driams-tophat [path]
(let [spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (:intensity spec))
_ (r `(<- s_th (createMassSpectrum :mass ~masses :intensity ~intensities)))
r-result (r->clj (r "intensity(removeBaseline(s_th, method='TopHat', halfWindowSize=100))"))
clj-result (signal/tophat-baseline (:intensity spec) {:half-window-size 100})
max-diff (apply max (map #(Math/abs (- (double %1) (double %2)))
r-result (seq clj-result)))]
{:file (last (clojure.string/split path #"/")) :n-points (count r-result) :max-diff max-diff}))(def driams-th-results (mapv compare-driams-tophat driams-files))(kind/table (tc/dataset driams-th-results))| file | n-points | max-diff |
|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 20813 | 0.0 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 20820 | 0.0 |
| 0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz | 20825 | 0.0 |
(def driams-th-all-pass?
(every? #(< (:max-diff %) 1e-10) driams-th-results))driams-th-all-pass?trueDRIAMS: Median Calibration Comparison
Median calibration divides every intensity by the spectrum’s median. Tested on raw DRIAMS spectra (no preprocessing) to isolate the median calibration step from SG edge effects.
(defn compare-driams-medcal [path]
(let [spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (:intensity spec))
_ (r `(<- s_mc (createMassSpectrum :mass ~masses :intensity ~intensities)))
r-result (r->clj (r "intensity(calibrateIntensity(s_mc, method='median'))"))
clj-result (signal/median-calibrate (:intensity spec) {})
max-diff (apply max (map #(Math/abs (- (double %1) (double %2)))
r-result (seq clj-result)))]
{:file (last (clojure.string/split path #"/")) :n-points (count r-result) :max-diff max-diff}))(def driams-mc-results (mapv compare-driams-medcal driams-files))(kind/table (tc/dataset driams-mc-results))| file | n-points | max-diff |
|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 20813 | 0.0 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 20820 | 0.0 |
| 0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz | 20825 | 0.0 |
(def driams-mc-all-pass?
(every? #(< (:max-diff %) 1e-10) driams-mc-results))driams-mc-all-pass?trueDRIAMS: Trim Comparison
Trimming a spectrum to a mass range should match MALDIquant in both row count and mass range boundaries.
(defn compare-driams-trim [path]
(let [spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (:intensity spec))
_ (r `(<- s_tr (createMassSpectrum :mass ~masses :intensity ~intensities)))
r-count (long (first (r->clj (r "length(mass(trim(s_tr, range=c(4000, 12000))))"))))
r-min (first (r->clj (r "min(mass(trim(s_tr, range=c(4000, 12000))))")))
r-max (first (r->clj (r "max(mass(trim(s_tr, range=c(4000, 12000))))")))
clj-trimmed (signal/trim-spectrum spec {:range [4000.0 12000.0]})
clj-count (tc/row-count clj-trimmed)
clj-min (apply min (seq (:mass clj-trimmed)))
clj-max (apply max (seq (:mass clj-trimmed)))]
{:file (last (clojure.string/split path #"/"))
:count-match? (= r-count clj-count)
:min-match? (= r-min clj-min)
:max-match? (= r-max clj-max)}))(def driams-trim-results (mapv compare-driams-trim driams-files))(kind/table (tc/dataset driams-trim-results))| file | count-match? | min-match? | max-match? |
|---|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | true | true | true |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | true | true | true |
| 0006206b-ddac-46e1-9c73-93beae3552ef_MALDI1.txt.gz | true | true | true |
(def driams-trim-all-pass?
(every? #(and (:count-match? %) (:min-match? %) (:max-match? %))
driams-trim-results))driams-trim-all-pass?trueDRIAMS: Peak Detection — SNR Sweep
Compare peak counts at multiple SNR thresholds (1, 2, 3, 5) on five DRIAMS spectra. This extends the “DRIAMS Peak Detection Comparison” section which used a single threshold.
(def driams-files-5
(take 5 (sort (.list (clojure.java.io/file "DRIAMS/DRIAMS-A/raw/2018")))))(defn compare-driams-peaks-snr [filename]
(let [path (str "DRIAMS/DRIAMS-A/raw/2018/" filename)
spec (load-driams-spectrum path)
masses (vec (:mass spec))
intensities (vec (:intensity spec))
_ (r `(<- s_pk (createMassSpectrum :mass ~masses :intensity ~intensities)))
_ (r "s_pk_proc <- calibrateIntensity(removeBaseline(smoothIntensity(transformIntensity(s_pk, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5), method='SNIP', iterations=25), method='TIC')")
clj-proc (signal/preprocess-spectrum-data
{:mass masses :intensity intensities}
{:should-sqrt-transform true :smooth-window 11 :smooth-polynomial 2
:baseline-iterations 25 :baseline-repetitions 1
:should-tic-normalize true :tic-target 1.0})
results (mapv (fn [snr]
(let [r-n (long (first (r->clj (r (format "length(mass(detectPeaks(s_pk_proc, SNR=%d, halfWindowSize=20)))" snr)))))
clj-n (count (signal/detect-peaks (:intensity clj-proc) {:half-window-size 20 :snr snr}))]
{:snr snr :r-count r-n :clj-count clj-n :diff (Math/abs (- r-n clj-n))}))
[1 2 3 5])]
{:file filename :results results
:all-close? (every? #(<= (:diff %) 1) results)}))(def driams-snr-results (mapv compare-driams-peaks-snr driams-files-5))(kind/table
(tc/dataset
(for [{:keys [file results]} driams-snr-results
{:keys [snr r-count clj-count diff]} results]
{:file file :snr snr :r-count r-count :clj-count clj-count :diff diff})))| file | snr | r-count | clj-count | diff |
|---|---|---|---|---|
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 1 | 469 | 469 | 0 |
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 2 | 452 | 452 | 0 |
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 3 | 270 | 270 | 0 |
| 00006690-1411-4a89-87cc-ab84678cc9fb_MALDI1.txt.gz | 5 | 77 | 77 | 0 |
| 0000ecf4-e821-4772-acb5-87db759edcae_MALDI2.txt.gz | 1 | 265 | 265 | 0 |
| 0000ecf4-e821-4772-acb5-87db759edcae_MALDI2.txt.gz | 2 | 211 | 211 | 0 |
| 0000ecf4-e821-4772-acb5-87db759edcae_MALDI2.txt.gz | 3 | 134 | 134 | 0 |
| 0000ecf4-e821-4772-acb5-87db759edcae_MALDI2.txt.gz | 5 | 87 | 86 | 1 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 1 | 424 | 424 | 0 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 2 | 252 | 252 | 0 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 3 | 166 | 166 | 0 |
| 0001ddde-ac83-4877-97f9-7651de40ccb6_MALDI1.txt.gz | 5 | 114 | 114 | 0 |
| 000297cd-7cea-421b-ac23-b1a095a7c976_MALDI1.txt.gz | 1 | 443 | 442 | 1 |
| 000297cd-7cea-421b-ac23-b1a095a7c976_MALDI1.txt.gz | 2 | 376 | 375 | 1 |
| 000297cd-7cea-421b-ac23-b1a095a7c976_MALDI1.txt.gz | 3 | 200 | 199 | 1 |
| 000297cd-7cea-421b-ac23-b1a095a7c976_MALDI1.txt.gz | 5 | 96 | 96 | 0 |
| 0002ec87-db65-42fb-910e-ebc5fe4429cf_MALDI2.txt.gz | 1 | 281 | 281 | 0 |
| 0002ec87-db65-42fb-910e-ebc5fe4429cf_MALDI2.txt.gz | 2 | 178 | 178 | 0 |
| 0002ec87-db65-42fb-910e-ebc5fe4429cf_MALDI2.txt.gz | 3 | 135 | 135 | 0 |
| 0002ec87-db65-42fb-910e-ebc5fe4429cf_MALDI2.txt.gz | 5 | 94 | 94 | 0 |
(def driams-snr-all-pass?
(every? :all-close? driams-snr-results))driams-snr-all-pass?trueSynthetic Stress: 50K-Point Spectrum
A large synthetic spectrum with 50,000 mass points exercises buffer boundary conditions and numerical stability of the full pipeline.
(r "set.seed(42)
n_50k <- 50000
masses_50k <- seq(2000, 20000, length.out=n_50k)
baseline_50k <- 500 * exp(-(masses_50k - 2000) / 3000)
peaks_50k <- numeric(n_50k)
peak_centers_50k <- seq(3000, 18000, by=500)
for (c in peak_centers_50k) {
peaks_50k <- peaks_50k + runif(1, 100, 1000) * exp(-((masses_50k - c)^2) / (2 * 20^2))
}
noise_50k <- abs(rnorm(n_50k, 0, 5))
raw_50k <- baseline_50k + peaks_50k + noise_50k
s_50k <- createMassSpectrum(mass=masses_50k, intensity=raw_50k)
proc_50k <- calibrateIntensity(removeBaseline(smoothIntensity(transformIntensity(s_50k, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5), method='SNIP', iterations=25), method='TIC')
r_result_50k <- intensity(proc_50k)") [1] 0.000000e+00 0.000000e+00 0.000000e+00 5.596144e-07 1.743882e-06
[6] 3.160293e-06 3.139494e-06 2.652225e-06 2.440836e-06 3.081876e-06
[11] 5.367919e-07 1.190245e-08 7.473152e-07 2.513336e-06 3.347085e-06
[16] 3.334504e-06 4.048316e-06 3.900386e-06 3.543433e-06 1.273350e-06
[21] 1.159541e-06 4.367975e-06 5.041370e-06 4.666990e-06 5.550952e-06
[26] 3.518272e-06 6.048297e-06 7.663340e-06 8.826245e-06 7.428131e-06
[31] 8.819785e-06 9.080158e-06 6.535544e-06 4.672128e-06 1.940296e-06
[36] 1.150646e-06 5.613873e-06 8.427923e-06 9.432432e-06 8.306793e-06
[41] 7.464598e-06 6.256616e-06 3.450818e-06 1.217233e-06 0.000000e+00
[46] 2.697047e-06 3.246624e-06 4.755633e-06 4.458869e-06 5.266299e-06
[51] 5.559314e-06 5.983452e-06 5.692583e-06 4.304518e-06 6.151698e-06
[56] 7.375162e-06 7.302139e-06 7.861688e-06 5.804180e-06 6.935159e-06
[61] 6.484146e-06 6.043872e-06 5.034623e-06 4.588138e-06 4.929203e-06
[66] 5.880068e-06 5.703163e-06 3.516002e-06 2.916432e-06 3.679615e-06
[71] 3.214713e-06 2.865584e-06 3.266547e-06 3.955414e-06 4.537737e-06
[76] 5.508159e-06 5.426927e-06 4.460925e-06 5.749672e-06 6.611580e-06
[81] 7.714147e-06 8.389717e-06 7.922294e-06 6.921407e-06 5.071007e-06
[86] 3.125119e-06 4.653050e-07 0.000000e+00 1.615903e-06 2.822992e-06
[91] 4.651903e-06 5.219311e-06 6.987473e-06 8.110713e-06 8.339380e-06
[96] 8.156473e-06 6.280849e-06 7.834775e-06 8.424132e-06 8.486846e-06
[101] 8.433788e-06 6.702707e-06 6.925235e-06 5.025371e-06 3.078970e-06
[106] 2.078154e-06 2.048236e-06 2.941006e-06 3.817994e-06 4.727210e-06
[111] 3.605475e-06 2.172075e-06 2.627495e-06 2.216590e-06 2.426365e-06
[116] 4.521428e-06 5.939519e-06 6.708965e-06 7.658844e-06 6.511993e-06
[121] 3.356863e-06 1.548241e-06 2.859381e-06 3.053563e-06 4.080756e-06
[126] 7.252520e-06 9.429145e-06 1.019059e-05 1.079271e-05 8.168772e-06
[131] 6.622732e-06 5.129565e-06 3.647855e-06 2.082697e-06 2.916686e-06
[136] 3.143376e-06 3.932774e-06 3.974859e-06 6.410473e-06 5.002036e-06
[141] 8.499242e-06 8.530931e-06 6.786503e-06 6.958122e-06 6.709815e-06
[146] 3.276635e-06 5.528617e-06 6.554140e-06 6.745754e-06 4.880707e-06
[151] 5.042332e-06 4.217356e-06 1.654740e-06 2.372729e-06 1.448292e-06
[156] 1.401048e-06 3.879429e-06 4.940334e-06 3.195642e-06 3.561398e-06
[161] 2.643565e-06 1.288340e-06 1.452289e-06 2.083174e-06 2.518901e-06
[166] 4.120904e-06 3.305134e-06 3.313841e-06 3.164158e-06 5.446094e-06
[171] 6.178460e-06 5.099713e-06 5.837082e-06 3.798039e-06 4.435409e-06
[176] 5.304410e-06 3.971288e-06 3.827887e-06 3.261442e-06 3.213679e-06
[181] 4.839528e-06 4.029727e-06 1.946625e-06 1.151544e-06 2.231311e-06
[186] 3.423067e-06 2.335963e-06 1.567698e-06 1.933691e-07 0.000000e+00
[191] 2.751910e-06 2.493213e-06 1.625678e-06 3.653797e-06 5.057229e-06
[196] 4.983094e-06 2.754603e-06 7.895349e-07 1.804134e-06 2.909975e-06
[201] 3.567746e-06 3.209109e-06 4.447416e-06 5.442122e-06 4.874156e-06
[206] 4.829841e-06 3.131764e-06 2.308172e-06 3.247487e-06 2.532567e-06
[211] 5.963171e-07 1.051130e-06 3.079897e-06 2.714848e-06 2.892440e-06
[216] 3.222259e-06 5.052758e-06 8.022142e-06 8.304385e-06 8.309369e-06
[221] 8.553968e-06 7.676597e-06 8.057318e-06 5.498692e-06 2.103885e-06
[226] 8.249071e-07 2.099870e-06 2.358778e-06 1.371322e-06 8.357744e-07
[231] 1.955312e-06 2.221483e-06 3.360887e-06 3.578181e-06 4.424974e-06
[236] 6.567386e-06 6.640424e-06 5.739010e-06 4.711981e-06 3.039334e-06
[241] 1.830425e-06 1.172991e-06 1.429397e-06 1.478112e-06 2.989979e-06
[246] 3.856660e-06 4.995163e-06 4.576814e-06 5.188055e-06 4.418978e-06
[251] 1.904425e-06 2.653994e-06 3.679244e-06 3.618566e-06 4.984526e-06
[256] 4.923520e-06 4.755746e-06 3.909301e-06 5.272029e-06 4.369035e-06
[261] 3.635842e-06 3.810015e-06 5.423022e-06 5.903136e-06 6.051589e-06
[266] 5.257724e-06 2.715014e-06 5.213614e-06 6.178128e-06 8.292986e-06
[271] 8.186802e-06 8.901004e-06 1.054976e-05 7.653598e-06 7.465940e-06
[276] 5.902470e-06 2.612812e-06 7.193789e-06 7.320539e-06 9.288310e-06
[281] 7.931622e-06 7.832156e-06 6.492078e-06 2.534948e-06 3.175600e-06
[286] 1.634638e-06 0.000000e+00 1.009570e-06 1.203064e-06 2.286466e-06
[291] 2.804424e-06 3.120911e-06 3.618142e-06 3.505241e-06 4.345231e-06
[296] 3.108887e-06 1.618944e-06 4.503031e-07 1.452428e-06 1.569705e-06
[301] 1.138544e-06 1.212986e-06 2.498524e-06 3.726428e-06 3.305837e-06
[306] 2.807806e-06 4.653634e-06 6.462465e-06 6.253567e-06 5.977730e-06
[311] 4.800425e-06 5.206379e-06 5.781324e-06 5.889790e-06 4.370088e-06
[316] 3.860702e-06 5.031276e-06 4.065243e-06 2.855320e-06 3.258667e-06
[321] 1.808208e-06 1.750436e-06 3.304908e-06 4.697981e-06 6.175716e-06
[326] 6.088800e-06 6.573030e-06 6.476342e-06 6.066485e-06 6.796140e-06
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[1261] 5.154922e-06 4.345036e-06 1.706384e-06 0.000000e+00 3.484585e-07
[1266] 8.128549e-07 3.638937e-07 1.685192e-06 1.130806e-06 4.555393e-07
[1271] 1.820293e-06 3.731577e-06 4.424026e-06 3.986056e-06 4.143258e-06
[1276] 4.700533e-06 4.035272e-06 5.968322e-06 4.545087e-06 5.406240e-06
[1281] 7.500088e-06 7.392010e-06 9.196856e-06 8.803367e-06 8.152534e-06
[1286] 8.034044e-06 5.059931e-06 6.161369e-06 4.224058e-06 3.874619e-06
[1291] 5.172771e-06 4.695809e-06 7.384610e-06 7.070119e-06 5.756384e-06
[1296] 3.856292e-06 1.628708e-06 2.550663e-06 2.010895e-06 4.847577e-06
[1301] 8.987995e-06 1.067049e-05 1.036411e-05 9.320434e-06 6.644590e-06
[1306] 4.541652e-06 3.418067e-06 1.946686e-06 5.683145e-06 8.650117e-06
[1311] 1.139154e-05 1.250086e-05 1.204730e-05 1.006475e-05 8.791001e-06
[1316] 7.635252e-06 4.512345e-06 2.835385e-06 4.324434e-06 7.101834e-07
[1321] 9.516732e-07 1.739165e-06 2.373891e-06 1.601904e-06 2.587823e-06
[1326] 4.076339e-06 6.117871e-06 7.054896e-06 8.002541e-06 5.729047e-06
[1331] 7.646685e-06 8.716093e-06 7.518305e-06 5.847443e-06 4.310047e-06
[1336] 4.833241e-06 3.771584e-06 1.307134e-06 2.648433e-06 1.857940e-06
[1341] 2.698676e-06 2.494313e-06 3.025037e-06 4.625839e-06 5.020104e-06
[1346] 4.765387e-06 4.517706e-06 5.487370e-06 8.157868e-06 8.173919e-06
[1351] 5.871456e-06 5.635214e-06 5.243848e-06 4.095041e-06 2.434069e-06
[1356] 1.686310e-06 2.215813e-06 2.791634e-06 3.216380e-06 1.477718e-06
[1361] 1.921899e-06 2.081777e-06 1.858346e-06 1.905165e-06 2.221800e-06
[1366] 4.440693e-06 6.389886e-06 7.745105e-06 7.206734e-06 5.796099e-06
[1371] 6.366550e-06 3.202144e-06 2.422246e-06 1.340448e-06 0.000000e+00
[1376] 1.938805e-06 3.320762e-06 3.931119e-06 3.316917e-06 3.751189e-06
[1381] 3.372723e-06 2.415102e-06 3.255933e-06 3.305552e-06 3.389282e-06
[1386] 6.249010e-06 6.268899e-06 5.913105e-06 5.333470e-06 6.461561e-06
[1391] 7.317803e-06 7.966551e-06 6.821255e-06 6.063940e-06 5.470982e-06
[1396] 5.737981e-06 3.484517e-06 2.163189e-06 2.765485e-07 8.575253e-07
[1401] 2.725035e-07 8.252307e-07 0.000000e+00 1.362208e-06 4.755191e-06
[1406] 8.905696e-06 1.110348e-05 1.210770e-05 1.111554e-05 1.051645e-05
[1411] 8.314757e-06 5.942586e-06 3.015765e-06 2.177509e-06 1.808890e-06
[1416] 2.966529e-06 3.313926e-06 2.268573e-06 2.660823e-06 2.940715e-06
[1421] 3.967986e-06 4.132638e-06 4.419383e-06 7.093801e-06 6.456946e-06
[1426] 7.194668e-06 7.689051e-06 6.300361e-06 6.557913e-06 4.447108e-06
[1431] 3.096717e-06 2.290764e-06 0.000000e+00 1.569601e-06 2.017373e-06
[1436] 1.900998e-06 3.568843e-06 2.923898e-06 3.443210e-06 4.076006e-06
[1441] 4.616911e-06 4.427696e-06 3.932020e-06 4.259453e-06 5.594110e-06
[1446] 4.150349e-06 2.970265e-06 5.494390e-07 9.361661e-07 1.630953e-06
[1451] 2.332316e-06 1.875380e-06 2.540256e-06 4.913573e-07 1.661991e-06
[1456] 2.496235e-06 1.265490e-06 3.004077e-06 4.311506e-06 3.505168e-06
[1461] 4.237932e-06 3.668801e-06 3.849306e-06 9.944856e-07 2.678429e-06
[1466] 2.552203e-06 1.794976e-06 3.451402e-06 2.881478e-06 7.802131e-07
[1471] 3.446409e-06 3.844528e-06 5.151677e-06 5.165431e-06 5.161026e-06
[1476] 2.946301e-06 5.183478e-06 4.742350e-06 4.837669e-06 4.466696e-06
[1481] 5.583419e-06 6.068088e-06 6.386934e-06 5.299074e-06 2.822193e-06
[1486] 1.669045e-06 2.869922e-06 1.949832e-06 1.782839e-06 2.478199e-06
[1491] 2.634259e-06 3.334826e-06 1.991137e-06 1.360217e-06 7.925606e-07
[1496] 1.230082e-06 1.113592e-06 6.481143e-07 1.310657e-06 9.492486e-07
[1501] 2.059993e-06 2.522401e-06 1.447288e-06 2.732048e-06 2.517752e-06
[1506] 4.905980e-06 4.553592e-06 3.119209e-06 4.809056e-06 3.310265e-06
[1511] 5.832719e-06 8.299015e-06 8.012939e-06 8.673031e-06 7.011319e-06
[1516] 8.533876e-06 5.715569e-06 5.287165e-06 5.218973e-06 4.300977e-06
[1521] 4.661571e-06 5.899978e-06 3.391115e-06 2.591028e-06 1.987636e-06
[1526] 4.358425e-06 6.789520e-06 8.983016e-06 8.864742e-06 9.867037e-06
[1531] 9.143018e-06 7.766691e-06 5.282283e-06 3.771782e-06 2.169534e-06
[1536] 2.277735e-06 2.596996e-06 6.817341e-07 2.092200e-06 2.417158e-06
[1541] 3.784611e-06 4.264122e-06 4.436567e-06 4.222167e-06 3.835578e-06
[1546] 2.892183e-06 3.346899e-06 5.211040e-06 8.172391e-06 7.784562e-06
[1551] 8.549837e-06 6.160372e-06 3.697512e-06 2.601167e-06 1.284142e-06
[1556] 0.000000e+00 0.000000e+00 1.467280e-06 1.467287e-06 5.130394e-07
[1561] 1.383370e-06 1.851923e-07 3.710026e-07 2.217333e-06 3.795943e-06
[1566] 4.374736e-06 4.962469e-06 6.071961e-06 5.038425e-06 3.339981e-06
[1571] 3.594780e-06 3.389237e-06 4.326686e-06 5.169637e-06 3.784507e-06
[1576] 5.234282e-06 6.540537e-06 6.223792e-06 4.794559e-06 3.459534e-06
[1581] 3.623522e-06 5.683406e-06 6.873712e-06 7.243596e-06 6.829503e-06
[1586] 8.074403e-06 9.490821e-06 8.720415e-06 6.597631e-06 5.678901e-06
[1591] 5.194017e-06 5.180898e-06 4.745797e-06 4.450108e-06 4.883736e-06
[1596] 5.911665e-06 6.298692e-06 6.900945e-06 6.529887e-06 6.995690e-06
[1601] 5.825329e-06 5.289307e-06 4.763943e-06 3.784026e-06 3.902285e-06
[1606] 3.526603e-06 1.521463e-06 3.002842e-06 2.381074e-06 7.989843e-08
[1611] 7.026494e-07 3.075413e-06 5.139504e-06 6.530593e-06 7.314247e-06
[1616] 7.273214e-06 8.021018e-06 8.232864e-06 4.476089e-06 2.103151e-06
[1621] 4.796332e-06 5.749928e-06 6.008824e-06 6.820257e-06 5.719528e-06
[1626] 5.853740e-06 5.492488e-06 4.528915e-06 2.779178e-06 3.496984e-06
[1631] 4.309252e-06 2.942380e-06 2.063689e-06 3.823597e-06 2.666681e-06
[1636] 4.753806e-06 5.127934e-06 5.670445e-06 6.153794e-06 4.899672e-06
[1641] 3.791531e-06 1.940036e-06 0.000000e+00 6.498774e-07 0.000000e+00
[1646] 3.372938e-06 5.337169e-06 6.924317e-06 6.594809e-06 7.646817e-06
[1651] 8.636087e-06 6.870951e-06 5.122310e-06 4.414209e-06 2.842629e-06
[1656] 5.575642e-06 8.169384e-06 9.420628e-06 8.194855e-06 8.224583e-06
[1661] 8.190284e-06 7.901950e-06 7.594080e-06 6.121353e-06 4.872743e-06
[1666] 5.883844e-06 6.002808e-06 4.101239e-06 1.944987e-06 0.000000e+00
[1671] 3.604256e-07 2.305974e-06 4.600565e-06 5.785397e-06 6.991419e-06
[1676] 6.305197e-06 7.047803e-06 6.740952e-06 6.238170e-06 5.211522e-06
[1681] 2.970615e-06 4.819577e-06 6.400192e-06 5.588164e-06 5.460189e-06
[1686] 4.683865e-06 5.826534e-06 7.000802e-06 6.526604e-06 7.751225e-06
[1691] 8.109193e-06 9.710537e-06 9.897937e-06 1.089019e-05 1.286351e-05
[1696] 1.457804e-05 1.456758e-05 1.398837e-05 1.369058e-05 1.451461e-05
[1701] 1.353914e-05 9.194118e-06 7.206272e-06 6.825567e-06 7.552481e-06
[1706] 6.653273e-06 5.331602e-06 4.635519e-06 5.972507e-06 6.165488e-06
[1711] 4.673243e-06 3.366497e-06 4.984118e-06 6.195495e-06 7.835853e-06
[1716] 9.036656e-06 1.129052e-05 1.312298e-05 1.383822e-05 1.117728e-05
[1721] 7.656927e-06 6.215078e-06 6.903473e-06 5.192497e-06 5.522396e-06
[1726] 5.119422e-06 7.668326e-06 8.690689e-06 7.838745e-06 5.514618e-06
[1731] 4.547939e-06 2.598738e-06 2.856324e-06 2.796209e-06 5.077278e-06
[1736] 3.672288e-06 6.933663e-06 7.843441e-06 8.625819e-06 8.628485e-06
[1741] 7.889260e-06 5.174422e-06 2.993040e-06 3.008492e-06 1.844202e-06
[1746] 0.000000e+00 1.186901e-06 2.633450e-06 4.246310e-06 5.866932e-06
[1751] 6.353714e-06 8.222808e-06 1.062144e-05 1.207770e-05 1.402805e-05
[1756] 1.355665e-05 1.347371e-05 1.256757e-05 8.201966e-06 6.793133e-06
[1761] 3.753856e-06 4.622570e-06 4.903965e-06 4.987785e-06 8.182095e-06
[1766] 7.021332e-06 8.688016e-06 8.291241e-06 6.021856e-06 6.707072e-06
[1771] 6.213205e-06 7.479431e-06 7.319289e-06 8.153510e-06 8.492501e-06
[1776] 6.250962e-06 5.671767e-06 3.206747e-06 1.324782e-07 1.677378e-06
[1781] 5.424138e-06 7.833792e-06 1.010947e-05 1.137563e-05 1.158808e-05
[1786] 9.800066e-06 7.389965e-06 3.314317e-06 0.000000e+00 1.001439e-06
[1791] 1.906158e-06 1.821710e-06 2.989313e-06 3.048909e-06 4.703773e-06
[1796] 4.725508e-06 4.401955e-06 3.809008e-06 2.887178e-06 3.977670e-06
[1801] 5.515893e-06 5.780148e-06 5.625724e-06 5.018875e-06 7.014717e-06
[1806] 6.954072e-06 5.400596e-06 6.404271e-06 7.537379e-06 9.970723e-06
[1811] 1.192882e-05 1.237847e-05 1.135391e-05 9.635893e-06 9.354638e-06
[1816] 7.242291e-06 3.537010e-06 2.705762e-06 1.196311e-06 1.008557e-06
[1821] 3.072637e-06 4.557356e-06 7.003165e-06 8.426725e-06 1.003639e-05
[1826] 1.015493e-05 8.098713e-06 6.009944e-06 2.381708e-06 1.677236e-07
[1831] 2.377803e-06 4.688100e-06 9.198026e-06 9.959470e-06 1.187246e-05
[1836] 1.211474e-05 1.039513e-05 7.848853e-06 2.724246e-06 0.000000e+00
[1841] 1.769922e-06 3.277038e-06 5.744177e-06 5.146978e-06 7.682874e-06
[1846] 8.523046e-06 8.867829e-06 7.339014e-06 5.021716e-06 4.788699e-06
[1851] 4.958397e-06 4.940443e-06 5.721057e-06 5.718597e-06 6.522207e-06
[1856] 7.033923e-06 7.942266e-06 6.869559e-06 5.500895e-06 5.530677e-06
[1861] 2.388810e-06 2.585168e-06 2.999667e-06 5.580728e-06 6.820601e-06
[1866] 8.433726e-06 8.409928e-06 7.911899e-06 6.851375e-06 5.794484e-06
[1871] 2.472491e-06 1.199884e-06 2.641174e-07 2.108148e-06 3.623875e-06
[1876] 3.979330e-06 3.304639e-06 3.589685e-06 4.687977e-06 5.597377e-06
[1881] 4.648150e-06 4.520609e-06 5.212252e-06 3.816778e-06 4.488919e-06
[1886] 5.672528e-06 4.941113e-06 5.081798e-06 5.977509e-06 7.075861e-06
[1891] 7.278920e-06 9.754202e-06 9.234126e-06 6.820626e-06 7.263710e-06
[1896] 7.649349e-06 5.897543e-06 6.020733e-06 6.147163e-06 5.454806e-06
[1901] 6.572062e-06 7.863202e-06 6.681954e-06 5.449324e-06 3.897768e-06
[1906] 2.921910e-06 2.082957e-06 2.514849e-06 4.070181e-06 4.208335e-06
[1911] 4.684155e-06 5.779639e-06 5.400032e-06 4.935083e-06 3.603334e-06
[1916] 3.123920e-06 6.350563e-07 8.554835e-07 3.184280e-06 3.126550e-06
[1921] 2.679639e-06 5.577059e-06 6.732827e-06 8.239501e-06 9.174767e-06
[1926] 9.131946e-06 7.638538e-06 7.897971e-06 7.557160e-06 4.659847e-06
[1931] 3.613860e-06 4.041989e-06 3.335054e-06 3.252229e-06 2.059183e-06
[1936] 1.955523e-06 1.741397e-06 9.012205e-07 8.952648e-07 0.000000e+00
[1941] 6.039012e-07 1.478684e-06 1.864075e-06 2.094970e-06 2.226905e-07
[1946] 2.989419e-06 3.975725e-06 4.244047e-06 4.829091e-06 4.536556e-06
[1951] 4.357152e-06 4.257183e-06 3.331632e-06 1.432975e-06 8.515602e-07
[1956] 2.335406e-06 3.180052e-06 6.015912e-06 7.598213e-06 1.013735e-05
[1961] 1.126325e-05 1.104419e-05 8.959313e-06 5.808603e-06 4.300765e-06
[1966] 1.555585e-06 1.637919e-07 4.927854e-07 1.079935e-06 2.413967e-06
[1971] 2.848484e-06 3.905438e-06 3.873352e-06 4.223390e-06 5.713658e-06
[1976] 4.974253e-06 5.114190e-06 5.089218e-06 5.615450e-06 4.721127e-06
[1981] 2.793828e-06 3.710190e-06 4.602813e-06 4.276995e-06 5.478886e-06
[1986] 6.370842e-06 9.577743e-06 9.909979e-06 9.529395e-06 9.207093e-06
[1991] 6.846925e-06 6.303802e-06 3.571291e-06 1.792329e-06 2.823008e-06
[1996] 3.013162e-06 4.082553e-06 5.823195e-06 5.521618e-06 8.105435e-06
[2001] 7.976834e-06 6.759807e-06 5.353537e-06 5.869583e-06 4.604409e-06
[2006] 2.143554e-06 1.131641e-06 1.559187e-06 0.000000e+00 1.666331e-06
[2011] 8.921396e-07 0.000000e+00 3.858213e-06 7.766656e-06 1.158790e-05
[2016] 1.369441e-05 1.531893e-05 1.462847e-05 1.451647e-05 1.301631e-05
[2021] 9.929888e-06 6.249132e-06 6.155431e-06 5.697797e-06 4.928483e-06
[2026] 3.693284e-06 3.069157e-06 2.918113e-06 3.295763e-06 4.764850e-06
[2031] 5.435978e-06 4.875811e-06 4.466095e-06 3.157735e-06 3.129464e-06
[2036] 3.574245e-06 3.147375e-06 3.297151e-06 2.342939e-06 5.429975e-06
[2041] 5.279293e-06 5.755857e-06 5.628359e-06 3.630533e-06 5.284766e-06
[2046] 4.738533e-06 2.620132e-06 3.202751e-06 5.033807e-06 6.388540e-06
[2051] 6.633909e-06 5.841150e-06 6.279066e-06 5.983639e-06 6.974314e-06
[2056] 5.366657e-06 4.011105e-06 4.650624e-06 4.087506e-06 2.411967e-06
[2061] 1.610211e-06 3.507141e-07 8.246880e-07 9.834064e-07 1.990567e-06
[2066] 2.240965e-06 2.540128e-06 3.812105e-06 4.320781e-06 4.089538e-06
[2071] 4.117279e-06 3.261312e-06 1.513034e-06 3.530029e-06 4.201468e-06
[2076] 5.239761e-06 6.367878e-06 8.244631e-06 8.204603e-06 7.611469e-06
[2081] 6.018462e-06 3.592705e-06 1.947454e-06 2.815294e-06 2.346866e-06
[2086] 4.146230e-06 6.159344e-06 7.531992e-06 8.376636e-06 8.633229e-06
[2091] 6.629159e-06 5.139104e-06 3.983960e-06 4.546346e-06 5.328108e-06
[2096] 5.617212e-06 7.441746e-06 9.315565e-06 9.124817e-06 8.668360e-06
[2101] 8.376341e-06 9.830446e-06 1.058548e-05 1.134453e-05 1.155767e-05
[2106] 1.113145e-05 1.035391e-05 9.785966e-06 6.509352e-06 4.256468e-06
[2111] 2.676921e-06 2.646294e-06 8.028929e-07 0.000000e+00 2.222736e-06
[2116] 3.582522e-06 5.367699e-06 7.211814e-06 8.288100e-06 8.668350e-06
[2121] 8.288347e-06 7.095343e-06 4.853399e-06 5.889062e-06 5.913316e-06
[2126] 7.383084e-06 8.401948e-06 9.572182e-06 1.003747e-05 8.290569e-06
[2131] 7.136562e-06 4.109066e-06 3.126860e-06 2.239958e-06 1.184261e-06
[2136] 2.469401e-06 7.618250e-07 4.200547e-06 5.309123e-06 5.388702e-06
[2141] 7.168660e-06 7.356812e-06 9.530858e-06 9.942259e-06 8.914700e-06
[2146] 8.754287e-06 7.890908e-06 8.191654e-06 7.133820e-06 6.844345e-06
[2151] 7.520194e-06 7.435946e-06 9.822935e-06 9.815772e-06 7.388517e-06
[2156] 7.140602e-06 5.698987e-06 3.149843e-06 2.246065e-06 6.736605e-07
[2161] 0.000000e+00 1.445198e-06 4.324229e-06 4.838886e-06 4.605883e-06
[2166] 3.774272e-06 5.166437e-06 4.600300e-06 3.939463e-06 2.935467e-06
[2171] 4.074764e-06 5.784246e-06 5.947323e-06 5.946790e-06 5.722276e-06
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[19706] 1.024467e-05 5.664214e-06 3.173451e-06 9.740900e-06 8.923199e-06
[19711] 1.044269e-05 1.242314e-05 1.244607e-05 1.144606e-05 8.287486e-06
[19716] 0.000000e+00 4.278382e-06 7.586460e-06 1.139123e-05 1.144783e-05
[19721] 1.302759e-05 1.354830e-05 1.490223e-05 1.588213e-05 1.706094e-05
[19726] 2.414809e-05 3.713518e-05 3.518459e-05 3.591403e-05 2.758103e-05
[19731] 2.070221e-05 1.710093e-05 1.081950e-05 7.980963e-06 1.357822e-05
[19736] 1.921300e-05 1.752063e-05 1.525056e-05 1.602104e-05 1.004768e-05
[19741] 7.495825e-06 7.899318e-06 0.000000e+00 6.261859e-06 9.395966e-06
[19746] 9.697391e-06 1.344418e-05 1.413067e-05 2.001153e-05 2.099699e-05
[19751] 1.747867e-05 1.437535e-05 1.283940e-05 2.128331e-05 2.224002e-05
[19756] 2.065888e-05 1.864568e-05 1.461570e-05 2.288478e-05 1.977360e-05
[19761] 1.324162e-05 1.260670e-05 1.716230e-05 1.857952e-05 1.544614e-05
[19766] 5.885281e-06 3.112563e-06 4.555275e-06 8.133794e-06 1.128189e-05
[19771] 1.244111e-05 1.599689e-05 2.041381e-05 2.032858e-05 1.474642e-05
[19776] 1.284732e-05 9.822559e-06 9.724067e-06 6.857204e-06 1.380841e-05
[19781] 2.192002e-05 2.955676e-05 3.124409e-05 3.262750e-05 2.904666e-05
[19786] 2.809198e-05 1.639403e-05 1.212487e-05 7.880242e-06 7.809503e-06
[19791] 1.180311e-05 1.470862e-05 1.231128e-05 1.633697e-05 1.571305e-05
[19796] 1.473135e-05 9.317585e-06 6.139354e-06 4.288540e-06 0.000000e+00
[19801] 1.274504e-06 1.919442e-06 7.137953e-06 1.247765e-05 1.255419e-05
[19806] 1.663656e-05 1.646161e-05 1.630511e-05 1.459225e-05 8.988368e-06
[19811] 1.341315e-05 1.261353e-05 1.640936e-05 1.787166e-05 1.702815e-05
[19816] 1.774001e-05 1.561860e-05 1.776669e-05 1.787284e-05 1.518392e-05
[19821] 1.782907e-05 1.243008e-05 1.103066e-05 1.400313e-05 1.041615e-05
[19826] 7.974905e-06 1.079093e-05 1.569103e-05 1.715003e-05 1.713244e-05
[19831] 1.523765e-05 1.245595e-05 1.107505e-05 8.760200e-06 0.000000e+00
[19836] 2.964260e-06 1.071899e-05 1.399567e-05 1.603032e-05 1.904525e-05
[19841] 2.007613e-05 1.491988e-05 1.310249e-05 9.486911e-06 2.724702e-06
[19846] 5.747730e-06 1.217066e-05 1.375618e-05 1.503863e-05 1.454209e-05
[19851] 1.149227e-05 9.252771e-06 8.587516e-06 9.550021e-06 9.933589e-06
[19856] 1.698649e-05 2.356288e-05 2.544727e-05 2.715164e-05 2.512381e-05
[19861] 1.990396e-05 1.648872e-05 9.130592e-06 6.752015e-06 0.000000e+00
[19866] 4.273005e-06 3.915067e-06 8.696439e-06 1.961737e-05 2.751724e-05
[19871] 3.140913e-05 3.015126e-05 2.974597e-05 1.990981e-05 1.107108e-05
[19876] 1.150558e-05 1.216717e-05 1.268496e-05 2.280843e-05 2.277748e-05
[19881] 2.549426e-05 2.554617e-05 2.465681e-05 1.756235e-05 1.509277e-05
[19886] 1.652991e-05 1.512378e-05 1.082445e-05 1.585346e-05 1.400244e-05
[19891] 1.647365e-05 1.930000e-05 1.824308e-05 1.637204e-05 1.045408e-05
[19896] 1.021238e-05 6.751595e-06 1.441283e-06 7.867790e-06 9.691763e-06
[19901] 9.992431e-06 1.191937e-05 1.350674e-05 1.226330e-05 8.954374e-06
[19906] 5.896477e-06 3.305702e-06 0.000000e+00 9.009415e-06 1.134595e-05
[19911] 1.222213e-05 1.660586e-05 1.914721e-05 2.026558e-05 1.802639e-05
[19916] 1.314082e-05 1.095037e-05 1.017372e-05 1.134979e-05 1.412783e-05
[19921] 1.244069e-05 1.616870e-05 1.787965e-05 2.060561e-05 1.733120e-05
[19926] 1.202537e-05 1.191275e-05 9.064715e-06 1.101087e-05 1.001710e-05
[19931] 9.798476e-06 1.064988e-05 1.530325e-05 1.234399e-05 1.044550e-05
[19936] 1.309638e-05 1.449592e-05 1.962474e-05 2.523595e-05 2.665171e-05
[19941] 2.798391e-05 2.725206e-05 2.319974e-05 1.543612e-05 1.328296e-05
[19946] 8.363885e-06 5.577300e-06 1.151403e-05 1.170304e-05 1.210478e-05
[19951] 1.183899e-05 1.280083e-05 8.995731e-06 9.562977e-06 1.084337e-05
[19956] 1.190842e-05 1.219872e-05 1.728985e-05 1.737280e-05 1.596452e-05
[19961] 1.775485e-05 1.480248e-05 9.816849e-06 7.453908e-06 2.035583e-06
[19966] 4.675394e-06 4.787938e-06 1.291310e-05 1.200591e-05 1.582932e-05
[19971] 2.287644e-05 2.641432e-05 2.326485e-05 1.772989e-05 1.032385e-05
[19976] 7.899939e-06 7.078406e-06 6.762372e-07 3.307796e-07 8.449695e-06
[19981] 1.420860e-05 1.626079e-05 1.582998e-05 1.933828e-05 2.273185e-05
[19986] 2.123898e-05 2.107283e-05 1.990549e-05 2.394402e-05 2.501982e-05
[19991] 2.336256e-05 1.495695e-05 1.283067e-05 1.692820e-05 1.692941e-05
[19996] 1.087095e-05 1.105444e-05 1.412906e-05 1.277520e-05 8.666842e-06
[20001] 1.084967e-05 8.606755e-06 9.879520e-06 1.289623e-05 1.419608e-05
[20006] 1.372398e-05 1.665412e-05 1.414544e-05 8.002729e-06 4.956257e-06
[20011] 5.189760e-06 6.651427e-07 0.000000e+00 3.389561e-06 9.959877e-06
[20016] 1.230805e-05 9.873380e-06 1.563346e-05 1.770199e-05 1.832844e-05
[20021] 1.639768e-05 1.633931e-05 1.900820e-05 2.136348e-05 2.017507e-05
[20026] 1.537856e-05 5.396676e-06 1.154354e-05 1.240343e-05 1.118823e-05
[20031] 1.206294e-05 1.226261e-05 1.342748e-05 1.601504e-05 2.019841e-05
[20036] 2.147273e-05 1.614428e-05 1.896122e-05 1.917567e-05 1.090572e-05
[20041] 1.223031e-05 7.428572e-06 3.447712e-06 1.199450e-05 1.967546e-05
[20046] 2.601127e-05 2.535973e-05 2.526401e-05 1.749562e-05 1.631128e-05
[20051] 2.072847e-05 1.603925e-05 1.491520e-05 2.342841e-05 2.100700e-05
[20056] 2.025947e-05 1.522205e-05 1.232156e-05 3.720565e-06 2.171867e-06
[20061] 2.753999e-06 1.694333e-06 1.128810e-05 1.655927e-05 1.730121e-05
[20066] 2.027254e-05 2.326182e-05 2.399855e-05 1.815701e-05 9.934422e-06
[20071] 4.831019e-06 1.235493e-06 8.791499e-06 1.331573e-05 1.769283e-05
[20076] 2.196906e-05 2.013201e-05 2.278453e-05 2.052267e-05 1.392394e-05
[20081] 1.864943e-06 3.933684e-06 7.664041e-06 1.123186e-05 1.462242e-05
[20086] 1.058826e-05 1.111612e-05 1.387913e-05 1.226448e-05 1.290275e-05
[20091] 1.462509e-05 2.212825e-05 2.366659e-05 2.339966e-05 2.437395e-05
[20096] 1.752609e-05 1.439021e-05 6.967589e-06 3.725559e-06 3.108627e-06
[20101] 4.749335e-06 4.872646e-06 2.811667e-06 0.000000e+00 5.297759e-06
[20106] 1.023241e-05 1.166926e-05 1.770946e-05 2.258297e-05 2.661624e-05
[20111] 2.511863e-05 2.353247e-05 1.816603e-05 1.143904e-05 1.412622e-05
[20116] 1.138431e-05 1.040392e-05 1.089252e-05 1.869718e-05 2.102910e-05
[20121] 2.084058e-05 2.465999e-05 2.353962e-05 2.048497e-05 2.118064e-05
[20126] 1.636044e-05 1.534076e-05 7.641950e-06 4.239234e-06 4.220341e-06
[20131] 1.118276e-05 1.332152e-05 1.428894e-05 1.140517e-05 1.583747e-05
[20136] 1.360428e-05 7.942822e-06 5.485189e-07 2.288979e-06 9.323642e-06
[20141] 1.656304e-05 1.609637e-05 1.137062e-05 1.090088e-05 1.847764e-05
[20146] 1.777949e-05 1.418895e-05 6.022423e-06 1.616411e-05 1.628219e-05
[20151] 2.007780e-05 1.824442e-05 1.282426e-05 1.195068e-05 1.259545e-05
[20156] 1.404452e-05 1.047202e-05 3.881458e-06 8.397104e-06 7.886782e-06
[20161] 7.402778e-06 8.631405e-06 6.124595e-06 1.633155e-06 1.111435e-06
[20166] 7.409088e-06 3.477908e-06 4.786254e-06 1.539739e-06 1.098717e-05
[20171] 1.315646e-05 1.620977e-05 1.637897e-05 1.719624e-05 1.567277e-05
[20176] 1.465697e-05 1.011315e-05 8.450945e-06 7.948149e-06 1.066105e-05
[20181] 7.224961e-06 9.099420e-06 9.118235e-06 9.484535e-06 1.179371e-05
[20186] 6.835904e-06 7.685633e-06 1.250171e-05 1.483511e-05 1.677908e-05
[20191] 1.976080e-05 1.938405e-05 1.527230e-05 1.419948e-05 1.845135e-05
[20196] 1.680134e-05 1.848611e-05 2.142866e-05 1.628130e-05 1.750511e-05
[20201] 1.109840e-05 3.826301e-06 0.000000e+00 1.669237e-06 6.215388e-06
[20206] 1.112360e-05 9.395715e-06 1.266631e-05 1.625693e-05 1.885565e-05
[20211] 2.123037e-05 2.130654e-05 1.987607e-05 1.633595e-05 9.050227e-06
[20216] 7.246737e-06 3.116261e-06 2.773070e-06 6.306820e-06 6.452868e-06
[20221] 8.149967e-06 4.156370e-06 1.193338e-05 1.058153e-05 1.263997e-05
[20226] 1.682758e-05 1.510716e-05 1.497169e-05 1.854027e-05 1.456560e-05
[20231] 1.227355e-05 1.047565e-05 1.111707e-05 7.243333e-06 6.569234e-06
[20236] 5.352347e-06 4.105186e-06 5.947148e-06 9.871045e-06 1.202793e-05
[20241] 1.432772e-05 1.635289e-05 1.072262e-05 9.768906e-06 3.172536e-06
[20246] 7.191020e-06 1.027163e-05 1.742874e-05 2.231037e-05 2.294158e-05
[20251] 2.764311e-05 2.694243e-05 2.170451e-05 2.272320e-05 1.661773e-05
[20256] 1.691066e-05 1.503729e-05 1.727551e-05 1.501785e-05 1.628080e-05
[20261] 2.582749e-05 2.444481e-05 2.470216e-05 2.562747e-05 1.866554e-05
[20266] 7.808293e-06 4.129090e-06 6.656287e-07 9.802970e-07 6.713249e-06
[20271] 1.514747e-05 1.949268e-05 1.983753e-05 1.758986e-05 1.328222e-05
[20276] 5.687086e-06 3.712213e-06 1.202088e-06 7.678325e-07 2.685071e-06
[20281] 7.456092e-06 9.490472e-06 9.337101e-06 9.747055e-06 8.771496e-06
[20286] 5.728405e-06 4.930671e-06 7.421221e-06 7.062134e-06 7.964416e-06
[20291] 0.000000e+00 3.836482e-06 9.378555e-06 1.765836e-05 2.517166e-05
[20296] 3.563378e-05 4.411070e-05 4.272923e-05 3.939058e-05 3.450205e-05
[20301] 2.057824e-05 2.043879e-05 1.245578e-05 9.258575e-06 6.788645e-06
[20306] 9.676936e-06 6.833524e-06 0.000000e+00 2.813593e-06 4.120766e-06
[20311] 3.628573e-06 5.493841e-06 9.029835e-06 1.098406e-05 1.035763e-05
[20316] 1.692718e-05 1.781015e-05 1.666052e-05 1.426065e-05 1.144169e-05
[20321] 8.694503e-06 1.100886e-05 1.055130e-05 9.538047e-06 8.925124e-06
[20326] 1.399824e-05 1.757849e-05 1.476972e-05 1.738184e-05 2.069223e-05
[20331] 1.558004e-05 1.669479e-05 1.384459e-05 1.236655e-05 8.427171e-06
[20336] 5.354291e-06 5.194978e-06 0.000000e+00 2.957934e-06 1.030604e-06
[20341] 2.018411e-06 7.795888e-06 5.751543e-06 1.099289e-05 1.474197e-05
[20346] 2.188477e-05 2.606444e-05 2.468194e-05 2.164368e-05 1.717839e-05
[20351] 1.743609e-05 1.132996e-05 8.203024e-06 1.289735e-05 1.225727e-05
[20356] 1.544296e-05 1.121128e-05 1.637640e-05 1.678585e-05 1.717439e-05
[20361] 1.701143e-05 1.411805e-05 1.507849e-05 7.451374e-06 4.360601e-06
[20366] 6.598674e-06 7.135208e-06 1.826655e-05 2.262867e-05 2.529225e-05
[20371] 2.328187e-05 2.025570e-05 1.813340e-05 1.278677e-05 1.399520e-05
[20376] 1.499992e-05 1.374934e-05 1.128463e-05 1.550947e-05 1.854495e-05
[20381] 2.025425e-05 2.062218e-05 2.152754e-05 1.952004e-05 1.373046e-05
[20386] 1.065647e-05 4.841711e-06 1.052188e-06 5.735442e-06 8.552514e-06
[20391] 7.744068e-06 6.276246e-06 8.182684e-06 7.090479e-06 5.122776e-06
[20396] 6.423951e-06 0.000000e+00 3.724916e-06 3.171644e-06 4.967797e-06
[20401] 4.068346e-06 5.254105e-06 1.037171e-05 1.295934e-05 1.802924e-05
[20406] 2.027601e-05 2.203554e-05 2.675060e-05 2.531710e-05 2.068514e-05
[20411] 1.590462e-05 1.099020e-05 1.240331e-05 1.125762e-05 1.538053e-05
[20416] 1.412510e-05 1.833376e-05 1.674701e-05 1.385031e-05 9.683665e-06
[20421] 6.479278e-06 3.601151e-06 1.602151e-05 2.026542e-05 2.713679e-05
[20426] 2.410557e-05 2.795628e-05 2.325618e-05 2.060378e-05 1.479898e-05
[20431] 5.283066e-06 5.221012e-06 1.393724e-05 1.123567e-05 1.080352e-05
[20436] 5.984094e-06 8.593812e-06 8.088920e-06 7.813760e-06 0.000000e+00
[20441] 2.495449e-06 1.026396e-05 1.198333e-05 8.399948e-06 1.402262e-05
[20446] 1.641070e-05 2.562997e-05 3.209175e-05 3.504651e-05 3.194325e-05
[20451] 3.576128e-05 3.707530e-05 2.794137e-05 2.117812e-05 2.467227e-05
[20456] 2.265018e-05 2.082419e-05 2.329983e-05 2.018886e-05 1.828356e-05
[20461] 1.702678e-05 1.547378e-05 1.232229e-05 9.651187e-06 1.161836e-05
[20466] 3.516390e-06 3.124145e-06 5.385899e-06 1.884472e-06 1.446715e-05
[20471] 2.199682e-05 2.513627e-05 2.528402e-05 1.674033e-05 1.475671e-05
[20476] 9.283173e-06 8.851900e-06 3.052252e-06 0.000000e+00 2.559958e-06
[20481] 5.436751e-06 8.966770e-06 6.341826e-06 5.397287e-06 1.121355e-05
[20486] 1.971596e-05 2.227072e-05 1.682041e-05 1.723491e-05 1.283935e-05
[20491] 1.381926e-05 1.034048e-05 7.339386e-06 7.789066e-06 7.600866e-06
[20496] 8.684345e-06 8.580672e-06 9.663627e-06 1.565492e-05 1.969225e-05
[20501] 1.765106e-05 1.701103e-05 1.599835e-05 1.369426e-05 1.064655e-05
[20506] 1.058522e-05 1.113603e-05 1.039423e-05 8.739231e-06 1.157682e-05
[20511] 7.543802e-06 7.969186e-06 2.896902e-06 4.991501e-06 6.784558e-06
[20516] 1.211681e-05 1.150361e-05 1.527580e-05 2.048491e-05 2.020405e-05
[20521] 1.829476e-05 1.530293e-05 7.042785e-06 1.207084e-05 9.786361e-06
[20526] 1.139237e-05 1.189676e-05 1.284778e-05 1.006592e-05 1.127702e-05
[20531] 1.184145e-05 8.823386e-06 3.814423e-06 9.253286e-06 2.391252e-06
[20536] 1.001525e-06 2.662611e-06 1.754811e-06 5.818613e-06 1.129419e-05
[20541] 1.121971e-05 1.648719e-05 1.706815e-05 1.602440e-05 1.261002e-05
[20546] 1.217234e-05 9.240336e-06 1.177999e-05 1.101644e-05 5.573613e-06
[20551] 0.000000e+00 1.863917e-06 3.419478e-06 4.962792e-06 6.945373e-06
[20556] 6.795600e-06 7.114360e-06 1.382181e-05 8.573990e-06 9.261208e-06
[20561] 5.726636e-06 5.728426e-06 9.927594e-06 1.605644e-05 1.657323e-05
[20566] 1.805653e-05 1.775808e-05 1.632543e-05 8.699719e-06 6.640625e-06
[20571] 8.470226e-07 3.494501e-06 3.991323e-06 1.227160e-05 1.388936e-05
[20576] 2.073124e-05 2.444857e-05 2.567639e-05 2.348665e-05 1.635271e-05
[20581] 9.308557e-06 5.478871e-06 1.645951e-06 2.441157e-06 2.423369e-06
[20586] 9.619381e-06 1.441278e-05 1.524201e-05 1.932928e-05 1.535778e-05
[20591] 1.766419e-05 1.754691e-05 2.021338e-05 1.779922e-05 1.172013e-05
[20596] 1.167165e-05 8.314086e-06 0.000000e+00 5.953346e-06 7.644907e-06
[20601] 1.466657e-05 1.585154e-05 1.638590e-05 9.243118e-06 5.184741e-06
[20606] 9.046253e-06 5.018433e-06 5.909999e-07 1.081277e-05 1.273367e-05
[20611] 1.104832e-05 6.782798e-06 3.909543e-06 9.621861e-07 5.719725e-06
[20616] 1.214969e-05 1.396868e-05 1.521569e-05 1.544331e-05 1.042081e-05
[20621] 2.782578e-06 9.288004e-07 0.000000e+00 5.835412e-08 6.693696e-06
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[49161] 2.473558e-05 4.081713e-05 2.824378e-05 4.267465e-05 5.215398e-05
[49166] 5.301036e-05 6.654619e-05 7.694952e-05 8.234036e-05 6.988849e-05
[49171] 7.036330e-05 5.765115e-05 3.142000e-05 1.825896e-05 2.169859e-05
[49176] 2.925739e-05 3.755667e-05 3.385953e-05 2.637955e-05 1.901644e-05
[49181] 1.481982e-05 2.198309e-05 3.482988e-05 3.922896e-05 6.126804e-05
[49186] 8.003737e-05 7.600061e-05 5.356454e-05 4.481364e-05 3.072331e-05
[49191] 2.751094e-05 3.102928e-05 4.951932e-05 6.541631e-05 7.218781e-05
[49196] 7.402933e-05 4.832596e-05 3.507419e-05 3.514184e-05 4.432985e-05
[49201] 5.368962e-05 6.111869e-05 8.168806e-05 9.240200e-05 7.017922e-05
[49206] 4.908776e-05 4.076835e-05 3.156567e-05 1.211195e-05 3.139779e-05
[49211] 5.341167e-05 7.167286e-05 8.503936e-05 9.346282e-05 7.914612e-05
[49216] 6.582704e-05 6.582681e-05 3.514489e-05 5.612737e-06 1.827150e-06
[49221] 0.000000e+00 8.279802e-06 2.221861e-05 3.604244e-05 4.799799e-05
[49226] 6.172773e-05 7.283489e-05 6.754204e-05 7.867967e-05 8.068617e-05
[49231] 7.765452e-05 7.259455e-05 4.724701e-05 2.384678e-05 1.405837e-05
[49236] 1.702501e-05 8.195280e-06 5.248659e-06 2.154373e-05 3.918204e-05
[49241] 4.252821e-05 3.141357e-05 2.228793e-05 2.081070e-05 2.799851e-05
[49246] 3.130419e-05 3.517102e-05 2.263211e-05 2.842405e-05 4.300767e-05
[49251] 3.698974e-05 2.873419e-05 4.321192e-05 4.808449e-05 4.396965e-05
[49256] 3.812571e-05 3.463498e-05 3.526896e-05 4.725079e-05 4.923373e-05
[49261] 3.442549e-05 2.798065e-05 4.007957e-05 5.367332e-05 5.840781e-05
[49266] 7.201399e-05 7.388809e-05 9.391172e-05 7.964769e-05 6.598520e-05
[49271] 4.311750e-05 4.243619e-05 4.287560e-05 4.024302e-05 4.361694e-05
[49276] 6.597451e-05 6.538433e-05 6.967943e-05 5.146823e-05 4.010633e-05
[49281] 2.118460e-05 2.113497e-05 1.151004e-05 0.000000e+00 1.197573e-05
[49286] 3.133687e-05 4.482753e-05 6.038364e-05 6.545089e-05 6.847182e-05
[49291] 5.964373e-05 7.521326e-05 6.371909e-05 4.847281e-05 5.756582e-05
[49296] 5.186768e-05 4.245042e-05 3.481108e-05 3.283332e-05 3.899788e-05
[49301] 4.584837e-05 6.225397e-05 6.515186e-05 6.703224e-05 6.969096e-05
[49306] 5.106413e-05 2.759434e-05 1.812680e-05 1.194458e-05 0.000000e+00
[49311] 1.963348e-06 0.000000e+00 2.089731e-05 1.973412e-05 3.219506e-05
[49316] 5.274443e-05 6.771717e-05 8.902890e-05 8.877779e-05 8.191224e-05
[49321] 7.187786e-05 6.555531e-05 6.936660e-05 4.949703e-05 5.806565e-05
[49326] 5.924514e-05 6.465273e-05 6.391495e-05 6.391872e-05 6.042208e-05
[49331] 5.352138e-05 6.954867e-05 6.735608e-05 4.799549e-05 3.714305e-05
[49336] 4.786852e-06 1.474952e-05 7.199306e-06 3.117005e-05 3.360480e-05
[49341] 5.434157e-05 7.197448e-05 8.860635e-05 8.592118e-05 9.127150e-05
[49346] 7.293698e-05 7.693951e-05 5.505576e-05 5.881730e-05 4.757138e-05
[49351] 2.842308e-05 1.520772e-05 2.842256e-05 1.650437e-05 4.597258e-05
[49356] 5.360334e-05 6.220192e-05 6.918773e-05 7.405731e-05 5.978641e-05
[49361] 3.867421e-05 3.315276e-05 1.195485e-05 0.000000e+00 1.349895e-05
[49366] 1.501665e-05 1.667073e-05 1.522213e-05 1.526131e-05 1.180210e-05
[49371] 2.256276e-05 3.521404e-05 2.907035e-05 4.472701e-05 4.408860e-05
[49376] 4.506976e-05 4.367568e-05 5.455801e-05 4.908131e-05 5.222518e-05
[49381] 4.488706e-05 6.385976e-05 6.602927e-05 6.541412e-05 6.339575e-05
[49386] 4.964393e-05 4.435453e-05 5.609490e-05 3.824921e-05 3.033629e-05
[49391] 1.062493e-05 1.479889e-05 1.944254e-05 2.212054e-05 2.508075e-05
[49396] 1.519259e-05 3.031697e-05 3.522700e-05 3.696015e-05 4.108512e-05
[49401] 4.256309e-05 3.272270e-05 5.076669e-05 5.860674e-05 6.288503e-05
[49406] 6.275173e-05 6.068584e-05 4.899603e-05 3.931896e-05 2.028376e-05
[49411] 2.771896e-06 0.000000e+00 2.363612e-05 3.074509e-05 5.421000e-05
[49416] 5.991451e-05 6.697960e-05 5.863577e-05 4.739857e-05 3.702563e-05
[49421] 3.405264e-05 6.012473e-05 6.483461e-05 6.171015e-05 5.509377e-05
[49426] 4.217945e-05 4.080239e-05 7.026544e-06 6.812844e-06 1.203705e-05
[49431] 6.617934e-06 3.225724e-05 4.422579e-05 5.720699e-05 5.467856e-05
[49436] 5.935084e-05 7.489688e-05 6.986544e-05 5.870929e-05 4.308077e-05
[49441] 2.662080e-05 1.586865e-05 6.195218e-06 2.815958e-06 4.091866e-06
[49446] 1.185908e-05 2.376081e-05 1.709009e-05 1.564650e-05 1.367782e-05
[49451] 2.433688e-05 1.614208e-05 1.727296e-05 2.485755e-05 3.153738e-05
[49456] 4.383558e-05 4.111098e-05 5.038494e-05 6.796071e-05 8.030026e-05
[49461] 9.725274e-05 1.002219e-04 9.532795e-05 8.600632e-05 7.025928e-05
[49466] 7.137806e-05 5.000720e-05 4.134317e-05 4.717251e-05 4.116353e-05
[49471] 4.260840e-05 3.397081e-05 2.864737e-05 3.510491e-05 3.732660e-05
[49476] 3.765380e-05 2.326619e-05 1.780933e-05 3.724743e-05 3.479549e-05
[49481] 2.818526e-05 2.282553e-05 1.490894e-05 1.959720e-05 2.748822e-05
[49486] 3.601992e-05 5.311340e-05 7.145027e-05 8.714396e-05 8.657932e-05
[49491] 6.320876e-05 6.598443e-05 3.851920e-05 2.592838e-05 2.666575e-05
[49496] 3.393958e-05 5.672994e-05 6.334836e-05 6.823067e-05 7.111359e-05
[49501] 6.591600e-05 6.215023e-05 4.283320e-05 4.293278e-05 3.392606e-05
[49506] 4.763244e-05 5.904262e-05 5.530581e-05 5.103925e-05 4.591993e-05
[49511] 2.102734e-05 4.615858e-06 0.000000e+00 2.401699e-06 1.775818e-06
[49516] 4.125730e-05 5.269926e-05 6.074285e-05 4.611907e-05 5.151378e-05
[49521] 3.265066e-05 3.527422e-05 3.851779e-05 2.971117e-05 2.655295e-05
[49526] 3.982765e-05 2.454871e-05 2.266479e-05 1.320649e-05 3.041075e-05
[49531] 2.463783e-05 4.715638e-05 5.445936e-05 4.884686e-05 3.237166e-05
[49536] 2.697116e-05 5.765327e-06 0.000000e+00 2.452200e-06 1.306666e-05
[49541] 1.265701e-05 4.451115e-05 3.958284e-05 5.070640e-05 4.831873e-05
[49546] 4.770909e-05 5.301835e-05 4.076947e-05 4.478125e-05 4.909452e-05
[49551] 4.247407e-05 4.018627e-05 3.015115e-05 2.578067e-05 1.805541e-05
[49556] 1.988639e-05 3.375921e-05 1.064628e-05 2.200873e-05 2.782144e-05
[49561] 2.881978e-05 2.527265e-05 3.033706e-05 3.713492e-05 4.508021e-05
[49566] 4.777661e-05 5.112141e-05 5.624783e-05 6.942496e-05 6.658268e-05
[49571] 7.868041e-05 7.996058e-05 8.179615e-05 1.009875e-04 8.524562e-05
[49576] 7.257900e-05 6.119499e-05 6.258492e-05 3.781717e-05 2.186096e-05
[49581] 1.131638e-05 1.846047e-05 2.616364e-05 4.222896e-05 3.929053e-05
[49586] 4.843531e-05 6.364095e-05 6.349871e-05 5.230564e-05 4.941341e-05
[49591] 4.117201e-05 5.494855e-05 6.475747e-05 5.204498e-05 4.225353e-05
[49596] 4.937490e-05 4.905618e-05 4.445946e-05 3.799723e-05 3.260266e-05
[49601] 3.703869e-05 3.824793e-05 5.370970e-05 4.928610e-05 4.413543e-05
[49606] 4.016327e-05 2.368472e-05 2.189612e-05 1.313315e-05 1.897722e-05
[49611] 3.934900e-05 5.225871e-05 7.045056e-05 7.130455e-05 5.699979e-05
[49616] 3.394215e-05 1.948338e-05 1.254753e-06 5.099691e-06 0.000000e+00
[49621] 1.056255e-05 1.089591e-05 2.612563e-05 3.321916e-05 3.520151e-05
[49626] 2.722523e-05 4.120555e-05 3.800866e-05 3.412803e-05 2.041197e-05
[49631] 3.178670e-05 4.369279e-05 5.447754e-05 6.424263e-05 7.942874e-05
[49636] 7.400028e-05 7.361141e-05 5.267455e-05 3.337819e-05 3.656568e-05
[49641] 4.514278e-05 4.155922e-05 2.112541e-05 2.491888e-05 4.476215e-05
[49646] 3.625088e-05 4.016768e-05 3.296886e-05 3.045989e-05 3.500395e-05
[49651] 3.166778e-05 4.534442e-06 8.268697e-06 8.656808e-06 2.478977e-05
[49656] 2.515431e-05 3.200669e-05 5.684454e-05 7.062655e-05 6.938731e-05
[49661] 6.749308e-05 4.601630e-05 5.456194e-05 4.560843e-05 3.959362e-05
[49666] 2.674037e-05 2.042779e-05 4.691926e-05 3.309346e-05 3.310111e-05
[49671] 2.844429e-05 2.352659e-05 3.313229e-05 3.243508e-05 3.136582e-05
[49676] 2.707351e-05 4.450277e-05 5.561338e-05 3.894340e-05 4.690867e-05
[49681] 3.904208e-05 3.871317e-05 4.299857e-05 3.756711e-05 5.701156e-05
[49686] 6.309488e-05 8.530610e-05 8.094828e-05 6.153022e-05 4.405292e-05
[49691] 2.657552e-05 2.154481e-05 2.424846e-05 3.603786e-05 5.597381e-05
[49696] 4.950767e-05 5.783979e-05 6.648337e-05 5.780472e-05 5.867154e-05
[49701] 6.886794e-05 7.370930e-05 7.717985e-05 8.492721e-05 7.763701e-05
[49706] 4.759062e-05 3.324910e-05 2.482958e-05 0.000000e+00 5.590201e-06
[49711] 9.291083e-06 5.675598e-06 2.927223e-05 5.858354e-05 6.251044e-05
[49716] 5.271601e-05 3.787114e-05 2.625278e-05 4.196798e-06 1.511362e-05
[49721] 1.980194e-05 1.295415e-05 3.818590e-05 4.119597e-05 5.565973e-05
[49726] 5.694411e-05 4.053984e-05 3.080684e-05 2.153426e-05 2.789892e-05
[49731] 3.575750e-05 2.795854e-05 5.305889e-05 5.309499e-05 6.196182e-05
[49736] 5.812725e-05 5.151619e-05 3.463176e-05 3.622106e-05 3.448676e-05
[49741] 2.586732e-05 3.274479e-05 3.777544e-05 3.331176e-05 4.857573e-06
[49746] 2.060466e-05 2.724604e-05 1.883633e-05 4.515678e-05 4.986866e-05
[49751] 3.369468e-05 4.673084e-05 5.048455e-05 4.283006e-05 3.022750e-05
[49756] 5.081123e-05 6.431438e-05 6.139813e-05 6.655750e-05 6.166376e-05
[49761] 3.676994e-05 3.543505e-05 2.499696e-05 1.946654e-05 2.034247e-05
[49766] 4.433158e-05 6.324662e-05 6.908442e-05 6.260045e-05 7.215567e-05
[49771] 7.498573e-05 6.742523e-05 5.472519e-05 5.371518e-05 6.045050e-05
[49776] 6.803987e-05 4.602444e-05 4.885734e-05 4.328194e-05 5.715673e-05
[49781] 6.179457e-05 5.136977e-05 4.487369e-05 3.480863e-05 3.848254e-05
[49786] 2.828635e-05 6.675842e-06 5.201164e-06 1.700633e-05 2.342068e-05
[49791] 2.811955e-05 5.002046e-05 5.167003e-05 5.848702e-05 5.668059e-05
[49796] 5.355726e-05 2.542870e-05 2.328725e-07 5.740201e-06 4.320488e-06
[49801] 1.782942e-05 3.052539e-05 2.240674e-05 3.556929e-05 2.957486e-05
[49806] 2.135938e-05 1.877336e-05 1.866043e-05 2.135104e-05 2.780497e-05
[49811] 2.662585e-05 2.079017e-05 1.979330e-05 2.676937e-05 2.675898e-05
[49816] 4.324530e-05 4.865056e-05 4.807005e-05 4.826225e-05 5.297279e-05
[49821] 4.390467e-05 4.158531e-05 3.232050e-05 2.516976e-05 2.123421e-05
[49826] 2.504120e-05 3.631818e-05 3.798896e-05 5.655484e-05 7.264033e-05
[49831] 7.069250e-05 7.613411e-05 6.371120e-05 5.062827e-05 2.834931e-05
[49836] 2.504372e-05 3.488212e-05 2.019201e-05 3.091165e-05 3.868539e-05
[49841] 2.494562e-05 3.686743e-05 3.392039e-05 2.806116e-05 1.471177e-05
[49846] 0.000000e+00 9.052282e-06 2.193237e-05 3.619804e-05 4.561487e-05
[49851] 4.963074e-05 5.279132e-05 4.840269e-05 3.870824e-05 1.669043e-05
[49856] 1.096707e-05 3.731326e-05 5.126522e-05 7.656079e-05 8.959753e-05
[49861] 7.712855e-05 5.942688e-05 5.056985e-05 6.235354e-05 5.338089e-05
[49866] 6.997612e-05 8.243170e-05 8.993109e-05 1.030573e-04 9.035190e-05
[49871] 6.421783e-05 4.469234e-05 3.752300e-05 3.747016e-05 1.972082e-05
[49876] 2.043279e-05 2.653731e-05 2.565394e-05 5.044553e-05 6.643714e-05
[49881] 1.063045e-04 1.238297e-04 1.218832e-04 1.231971e-04 1.125920e-04
[49886] 8.855580e-05 6.475269e-05 4.662252e-05 5.851484e-05 4.326925e-05
[49891] 7.783738e-05 9.401534e-05 9.339686e-05 1.142751e-04 1.010072e-04
[49896] 8.280068e-05 6.698049e-05 5.712195e-05 3.494240e-05 0.000000e+00
[49901] 1.151642e-05 1.312520e-05 2.339250e-05 3.788292e-05 3.485735e-05
[49906] 4.285020e-05 3.849780e-05 3.705446e-05 1.839917e-05 2.373947e-06
[49911] 0.000000e+00 1.108314e-05 1.080288e-05 3.189214e-05 6.095855e-05
[49916] 6.950466e-05 7.861086e-05 8.917702e-05 7.357101e-05 6.931791e-05
[49921] 5.621422e-05 3.896326e-05 1.884064e-05 2.153288e-05 4.219154e-05
[49926] 3.446129e-05 4.444104e-05 3.767154e-05 3.658123e-05 5.816725e-05
[49931] 7.449801e-05 8.464073e-05 1.070987e-04 1.168845e-04 1.267654e-04
[49936] 1.040261e-04 7.632006e-05 3.317219e-05 1.864095e-05 1.646397e-05
[49941] 1.189257e-05 1.142833e-05 1.999187e-05 3.840527e-05 2.939100e-05
[49946] 2.733523e-05 3.320023e-05 3.014154e-05 3.475110e-05 2.834598e-05
[49951] 2.219673e-05 4.487971e-05 5.942689e-05 7.487384e-05 5.916700e-05
[49956] 6.140251e-05 5.982168e-05 4.414923e-05 1.035838e-05 0.000000e+00
[49961] 2.025235e-05 5.639595e-05 6.322422e-05 7.583845e-05 7.476784e-05
[49966] 8.575221e-05 8.169879e-05 6.470271e-05 3.743738e-05 3.105031e-05
[49971] 2.186440e-05 1.050811e-05 0.000000e+00 6.949943e-06 5.982144e-06
[49976] 2.687413e-05 3.171553e-05 3.665423e-05 3.987244e-05 4.035055e-05
[49981] 3.217338e-05 2.674727e-05 2.292415e-05 2.106593e-05 1.660774e-05
[49986] 1.869309e-05 8.567344e-06 1.202280e-05 1.548231e-05 2.054528e-05
[49991] 1.793400e-05 3.103180e-05 2.559825e-05 3.688530e-05 4.016653e-05
[49996] 3.700852e-05 3.135901e-05 2.361633e-05 1.311214e-05 0.000000e+00
(def r-result-50k (r->clj (r "r_result_50k")))(def r-masses-50k (r->clj (r "masses_50k")))(def r-raw-50k (r->clj (r "raw_50k")))(count r-result-50k)50000(def clj-result-50k
(signal/preprocess-spectrum-data
{:mass (vec r-masses-50k) :intensity (vec r-raw-50k)}
{:should-sqrt-transform true :smooth-window 11 :smooth-polynomial 2
:baseline-iterations 25 :baseline-repetitions 1
:should-tic-normalize true :tic-target 1.0}))(def max-diff-50k
(apply max (map #(Math/abs (- (double %1) (double %2)))
r-result-50k (seq (:intensity clj-result-50k))))){:n-points (count r-result-50k) :max-diff max-diff-50k}{:n-points 50000, :max-diff 1.2704022942601028E-5}Synthetic Stress: Dense Peaks
A spectrum with ~127 densely-packed narrow peaks (30 Da spacing). Tests the peak detection algorithm’s ability to resolve closely-spaced peaks, especially at the SNR boundary.
(r "set.seed(123)
n_dense <- 10000
m_dense <- seq(2000, 6000, length.out=n_dense)
baseline_dense <- 50 * exp(-(m_dense - 2000) / 1000)
peaks_dense <- numeric(n_dense)
peak_positions_dense <- seq(2100, 5900, by=30)
for (c in peak_positions_dense) {
peaks_dense <- peaks_dense + runif(1, 10, 100) * exp(-((m_dense - c)^2) / (2 * 5^2))
}
noise_dense <- abs(rnorm(n_dense, 0, 2))
raw_dense <- baseline_dense + peaks_dense + noise_dense
s_dense <- createMassSpectrum(mass=m_dense, intensity=raw_dense)
proc_dense <- calibrateIntensity(removeBaseline(smoothIntensity(transformIntensity(s_dense, method='sqrt'), method='SavitzkyGolay', halfWindowSize=5), method='SNIP', iterations=25), method='TIC')
r_peaks_dense <- detectPeaks(proc_dense, SNR=2, halfWindowSize=20)")S4 class type : MassPeaks
Number of m/z values : 85
Range of m/z values : 2130.013 - 5850.385
Range of intensity values: 4.88e-04 - 9.776e-04
Range of snr values : 2.081 - 4.17
Memory usage : 3.125 KiB
(def r-dense-count (long (first (r->clj (r "length(mass(r_peaks_dense))")))))(def r-dense-masses (r->clj (r "mass(r_peaks_dense)")))Clojure side:
(def clj-dense-proc
(signal/preprocess-spectrum-data
{:mass (vec (r->clj (r "m_dense"))) :intensity (vec (r->clj (r "raw_dense")))}
{:should-sqrt-transform true :smooth-window 11 :smooth-polynomial 2
:baseline-iterations 25 :baseline-repetitions 1
:should-tic-normalize true :tic-target 1.0}))(def clj-dense-peaks
(signal/detect-peaks (:intensity clj-dense-proc) {:half-window-size 20 :snr 2}))(def clj-dense-count (count clj-dense-peaks))Compare peak counts:
{:r-count r-dense-count :clj-count clj-dense-count
:count-diff (Math/abs (- r-dense-count clj-dense-count))}{:r-count 85, :clj-count 85, :count-diff 0}Compare peak mass positions:
(def clj-dense-masses
(let [m (vec (:mass clj-dense-proc))]
(mapv #(nth m %) clj-dense-peaks)))(def dense-mass-max-diff
(if (= r-dense-count clj-dense-count)
(apply max (map #(Math/abs (- %1 %2)) r-dense-masses clj-dense-masses))
##Inf)){:mass-max-diff dense-mass-max-diff
:masses-match? (< dense-mass-max-diff 1e-6)}{:mass-max-diff 0.0, :masses-match? true}Summary
(def all-results
[["MAD Noise" (= r-mad clj-mad) (Math/abs (- clj-mad r-mad))]
["Peak Detection" (= r-peak-indices clj-peak-indices) 0.0]
["SNR Thresholds" (->> (compare-snr-thresholds [1 2 3 4]) (every? :match?)) 0.0]
["Sqrt Transform" (= r-sqrt-transformed clj-sqrt-transformed) 0.0]
["Savitzky-Golay (small)" sg-interior-match? (apply max (map #(Math/abs %) (map - clj-sg-smoothed r-sg-smoothed)))]
["SNIP Baseline" snip-match? (apply max (map #(Math/abs %) (map - clj-snip-corrected r-snip-corrected)))]
["TIC Normalize" tic-match? (apply max (map #(Math/abs %) (map - clj-tic-normalized r-tic-normalized)))]
["Full Pipeline (synthetic)" (< pipeline-max-diff 1e-4) pipeline-max-diff]
["Real Data (spectrum #1)" (< real-max-diff 1e-5) real-max-diff]
["Median Filter" median-interior-match? 0.0]
["Local MAD Noise" (< local-mad-max-diff 1e-10) local-mad-max-diff]
["Large Synthetic (10K)" (< large-max-diff 1e-5) large-max-diff]
["Overlapping Peaks" (= r-overlap-indices clj-overlap-indices) 0.0]
["High-Noise Peaks" (= r-noisy-peak-count (count clj-noisy-peaks)) 0.0]
["Constant Spectrum" (= r-const-peak-count (count clj-const-peaks)) 0.0]
["Non-Uniform TIC" (< nonuniform-max-diff 1e-10) nonuniform-max-diff]
["SG Param Sweep" (every? #(< (:max-interior-diff %) 1e-10) sg-sweep-results)
(apply max (map :max-interior-diff sg-sweep-results))]
["SNIP Iter Sweep" (every? #(< (:max-diff %) 1e-10) snip-sweep-results)
(apply max (map :max-diff snip-sweep-results))]
["Multi-Spectrum (16×)" multi-spectrum-all-pass?
(apply max (map :max-diff multi-spectrum-results))]
["DRIAMS Single (3×)" driams-single-all-pass?
(apply max (map :tic-diff driams-single-results))]
["DRIAMS Batch (10×)" driams-batch-all-pass?
(apply max (map :max-diff driams-batch-results))]
["DRIAMS Peaks (10×)" driams-peaks-close? 0.0]
["DRIAMS Cross-Site (4×)" driams-cross-site-all-pass?
(apply max (map :max-diff driams-cross-site-results))]
["Moving Average" (< ma-real-diff 1e-10) ma-real-diff]
["TopHat Baseline" (< th-real-diff 1e-10) th-real-diff]
["Median Calibration" (< medcal-real-diff 1e-10) medcal-real-diff]
["Trim" trim-match? 0.0]
["Bin+Filter (16×)" bin-filter-pass? 0.0]
["E2E Pipeline (16×)" e2e-close? e2e-feature-diff]
["DRIAMS Moving Avg (3×)" driams-ma-all-pass?
(apply max (map :max-diff driams-ma-results))]
["DRIAMS TopHat (3×)" driams-th-all-pass?
(apply max (map :max-diff driams-th-results))]
["DRIAMS Median Cal (3×)" driams-mc-all-pass?
(apply max (map :max-diff driams-mc-results))]
["DRIAMS Trim (3×)" driams-trim-all-pass? 0.0]
["DRIAMS Peaks SNR (5×4)" driams-snr-all-pass? 0.0]
["Stress: 50K Spectrum" (< max-diff-50k 1e-4) max-diff-50k]
["Dense Peaks (10K)" (< dense-mass-max-diff 1e-6) dense-mass-max-diff]])(kind/table
{:column-names ["Test" "Pass?" "Max Difference"]
:row-vectors all-results})| Test | Pass? | Max Difference |
|---|---|---|
| MAD Noise | true | 0.0 |
| Peak Detection | true | 0.0 |
| SNR Thresholds | true | 0.0 |
| Sqrt Transform | true | 0.0 |
| Savitzky-Golay (small) | true | 0.010000000000000217 |
| SNIP Baseline | true | 0.0 |
| TIC Normalize | true | 0.0 |
| Full Pipeline (synthetic) | true | 4.949591665173378E-5 |
| Real Data (spectrum #1) | true | 4.607880556342297E-6 |
| Median Filter | true | 0.0 |
| Local MAD Noise | true | 0.0 |
| Large Synthetic (10K) | true | 3.8524926758196015E-6 |
| Overlapping Peaks | true | 0.0 |
| High-Noise Peaks | true | 0.0 |
| Constant Spectrum | true | 0.0 |
| Non-Uniform TIC | true | 1.734723475976807E-18 |
| SG Param Sweep | true | 4.831690603168681E-13 |
| SNIP Iter Sweep | true | 0.0 |
| Multi-Spectrum (16×) | true | 2.2420487551652356E-5 |
| DRIAMS Single (3×) | true | 2.472680221974346E-5 |
| DRIAMS Batch (10×) | true | 9.338024234425511E-5 |
| DRIAMS Peaks (10×) | true | 0.0 |
| DRIAMS Cross-Site (4×) | true | 2.472680221974346E-5 |
| Moving Average | true | 2.9103830456733704E-11 |
| TopHat Baseline | true | 0.0 |
| Median Calibration | true | 0.0 |
| Trim | true | 0.0 |
| Bin+Filter (16×) | true | 0.0 |
| E2E Pipeline (16×) | true | 1 |
| DRIAMS Moving Avg (3×) | true | 1.8189894035458565E-12 |
| DRIAMS TopHat (3×) | true | 0.0 |
| DRIAMS Median Cal (3×) | true | 0.0 |
| DRIAMS Trim (3×) | true | 0.0 |
| DRIAMS Peaks SNR (5×4) | true | 0.0 |
| Stress: 50K Spectrum | true | 1.2704022942601028E-5 |
| Dense Peaks (10K) | true | 0.0 |
All tests pass:
(every? second all-results)true