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recursion-ninjarecursion-ninja
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Refactoring the 'replicate-results' into a makefile
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‎bin/README.md‎

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‎bin/measure-scaling-performance.sh‎

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#!/bin/bash
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# # # # #
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#
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# Function to call the requisite Haskell and Python binary/script (respectively)
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# which will generate the results data and figures presented in the paper.
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#
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# # # # #
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function using()
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{
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./bin/generate-timings $5 \
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--data "data-sets/$1.afasta" \
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--tree "data-sets/$1.tree" \
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--tcm "data-sets/tcm-${2}.tcm" \
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--output "$1-$2" \
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-n "$3" \
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-k "$4"
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python3 ./bin/plot-figure.py \
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"replicate-results/csv/${1}-${2}.preorder.csv" \
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"replicate-results/img/${1}-preorder.eps"
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python3 ./bin/plot-figure.py \
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"replicate-results/csv/${1}-${2}.postorder.csv" \
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"replicate-results/img/${1}-postorder.eps"
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}
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"$@"

‎plot-figure.py‎ renamed to ‎bin/plot-figure.py‎

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#!/bin/python3
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from mpl_toolkits import mplot3d
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import math
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import matplotlib.pyplot as plt

‎cabal.project‎

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allow-newer:
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base,
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Cabal,
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ghc-prim,
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template-haskell
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installdir: ./bin
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install-method: copy
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packages: .
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with-compiler: ghc-9.2.1
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‎makefile‎

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# # # # #
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#
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# Script to reproduce the results of the paper from a clean environment.
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# Installs all required dependencies. Builds the required binaries.
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# Run the experimental algorithm on datasets to generate the empirical results.
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#
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# # # # #
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bin-dir := ./bin
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data-dir := replicate-results
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executable := $(bin-dir)/generate-timings
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generate := $(bin-dir)/measure-scaling-performance.sh using
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biological-scale := [1%1,1%2,1%4,1%8,1%16,1%32]
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customized-scale := [1%1,1%2,1%4,1%8,1%16,1%32,1%64]
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customized-nodes := [4,8,16,32,64,128,256]
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# All synonyms for replicating the paper's results.
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all: results
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replicate: results
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reproduce: results
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results: fungi metazoa pathological
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# Install dependencies required to replicate results:
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ensure-Haskell: $(bin-dir)/generate-timings
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@command -v ghcup &> /dev/null || curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh
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ensure-Python:
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@command -v python3 &> /dev/null || apt-get install python3 --yes
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@command -v pip3 &> /dev/null || apt-get install python3-pip --yes
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@pip3 show matplotlib &> /dev/null || pip3 install --upgrade matplotlib
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ensure-R:
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command -v &> /dev/null || sudo apt install r-base
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compile-binaries: ensure-Haskell
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@echo "Copiling binaries"
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@ghcup run \
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--ghc 9.2.1 \
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--cabal 3.6.2.0 \
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-- cabal update && \
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cabal install \
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--installdir=$(bin-dir) \
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--install-method=copy
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ensure-workspace:
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@mkdir -p $(data-dir)/csv
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@mkdir -p $(data-dir)/data
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@mkdir -p $(data-dir)/img
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@mkdir -p $(data-dir)/taxa
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@mkdir -p $(data-dir)/tree
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fungi: compile-binaries ensure-Python ensure-R ensure-workspace
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$(generate) \
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'fungi' '11' '[25,50,100,200,400,800,1553]' $(biological-scale)
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metazoa: compile-binaries ensure-Python ensure-R ensure-workspace
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$(generate) \
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'metazoa' '11' '[25,50,100,200,400,800,1766]' $(biological-scale)
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pathological: compile-binaries ensure-Python ensure-R ensure-workspace
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$(generate) \
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'pathological' '12' $(customized-nodes) $(customized-scale)
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$(generate) \
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'pathological' '31' $(customized-nodes) $(customized-scale) '--no-generate'

‎replicate-results.sh‎

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#!/bin/bash
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#Script to run implied alingment data sets
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#load Haskell dependencies
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which stack || curl -sSL https://get.haskellstack.org/ | sh
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#load Python3 dependencies
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which python3 || apt-get install python3 --yes
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which pip3 || apt-get install python3-pip --yes
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pip3 show matplotlib || pip3 install --upgrade matplotlib
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# # # # #
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#
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# Script to reproduce the results of the paper from a clean environment.
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# Installs all required dependencies. Builds the required binaries.
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# Run the experimental algorithm on datasets to generate the empirical results.
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#
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# # # # #
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#load R dependencies
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which R || sudo apt install r-base
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#build binaries
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stack install
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# Install dependencies required to replicate results:
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# * Load Python3 dependencies
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which python3 &> /dev/null || apt-get install python3 --yes
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which pip3 &> /dev/null || apt-get install python3-pip --yes
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pip3 show matplotlib &> /dev/null || pip3 install --upgrade matplotlib
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#ensure target directories exist
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# * Load R dependencies
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which R &> /dev/null || sudo apt install r-base
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# * Load Haskell dependencies
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which ghcup &> /dev/null || curl --proto '=https' --tlsv1.2 -sSf https://get-ghcup.haskell.org | sh
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# Build binaries
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ghcup run \
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--ghc 9.2.1 \
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--cabal 3.6.2.0 \
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-- cabal update && \
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cabal install \
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--installdir=./bin \
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--install-method=copy
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# Ensure output directories exist to place results data
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mkdir -p replicate-results/csv
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mkdir -p replicate-results/data
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mkdir -p replicate-results/img
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mkdir -p replicate-results/taxa
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mkdir -p replicate-results/tree
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#run data sets
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./bin/generate-timings \
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--data data-sets/fungi.afasta \
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--tree data-sets/fungi.tree \
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--tcm data-sets/tcm-11.tcm \
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--output fungi-11 \
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-n [25,50,100,200,400,800,1553] \
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-k [1%1,1%2,1%4,1%8,1%16,1%32]
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python3 plot-figure.py replicate-results/csv/fungi-11.preorder.csv replicate-results/img/fungi-preorder.eps
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python3 plot-figure.py replicate-results/csv/fungi-11.postorder.csv replicate-results/img/fungi-postorder.eps
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./bin/generate-timings \
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--data data-sets/metazoa.afasta \
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--tree data-sets/metazoa.tree \
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--tcm data-sets/tcm-11.tcm \
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--output metazoa-11 \
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-n [25,50,100,200,400,800,1766] \
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-k [1%1,1%2,1%4,1%8,1%16,1%32]
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python3 plot-figure.py replicate-results/csv/metazoa-11.preorder.csv replicate-results/img/metazoa-preorder.eps
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python3 plot-figure.py replicate-results/csv/metazoa-11.postorder.csv replicate-results/img/metazoa-postorder.eps
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# Run data sets to produce the results data
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function generate()
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{
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./bin/generate-timings $5 \
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--data "data-sets/$1.afasta" \
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--tree "data-sets/$1.tree" \
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--tcm "data-sets/tcm-${2}.tcm" \
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--output "$1-$2" \
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-n "$3" \
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-k "$4"
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python3 plot-figure.py \
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"replicate-results/csv/${1}-${2}.preorder.csv" \
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"replicate-results/img/${1}-preorder.eps"
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./bin/generate-timings \
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--data data-sets/pathological.afasta \
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--tree data-sets/pathological.tree \
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--tcm data-sets/tcm-12.tcm \
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--output pathological-12 \
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-n [4,8,16,32,64,128,256] \
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-k [1%1,1%2,1%4,1%8,1%16,1%32,1%64]
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python3 plot-figure.py replicate-results/csv/pathological-12.preorder.csv replicate-results/img/pathological-12-preorder.eps
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python3 plot-figure.py replicate-results/csv/pathological-12.postorder.csv replicate-results/img/pathological-12-postorder.eps
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python3 plot-figure.py \
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"replicate-results/csv/${1}-${2}.postorder.csv" \
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"replicate-results/img/${1}-postorder.eps"
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}
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./bin/generate-timings \
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--no-generate \
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--data data-sets/pathological.afasta \
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--tree data-sets/pathological.tree \
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--tcm data-sets/tcm-31.tcm \
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--output pathological-31 \
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-n [4,8,16,32,64,128,256] \
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-k [1%1,1%2,1%4,1%8,1%16,1%32,1%64]
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python3 plot-figure.py replicate-results/csv/pathological-31.preorder.csv replicate-results/img/pathological-31-preorder.eps
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python3 plot-figure.py replicate-results/csv/pathological-31.postorder.csv replicate-results/img/pathological-31-postorder.eps
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generate 'fungi' '11' '[25,50,100,200,400,800,1553]' '[1%1,1%2,1%4,1%8,1%16,1%32]'
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generate 'metazoa' '11' '[25,50,100,200,400,800,1766]' '[1%1,1%2,1%4,1%8,1%16,1%32]'
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generate 'pathological' '12' '[4,8,16,32,64,128,256]' '[1%1,1%2,1%4,1%8,1%16,1%32,1%64]'
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generate 'pathological' '31' '[4,8,16,32,64,128,256]' '[1%1,1%2,1%4,1%8,1%16,1%32,1%64]' '--no-generate'

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