Nothing
tests/testthat/test_aleatory_analysis.R
In spartan: Simulation Parameter Analysis R Toolkit ApplicatioN: 'spartan'
library(spartan)
context("Test of Spartan Consistency Analysis")
test_that("read_simulation_results", {
# Simple function that reads in the simulation results from a CSV or R object
# The existance of the paths had been checked by pre-execution tests
# The R object is part of the package
data("tutorial_consistency_set")
expect_equal(nrow(read_simulation_results(getwd(), AA_SIM_RESULTS_FILE=NULL,
tutorial_consistency_set)),9060)
expect_equal(ncol(read_simulation_results(getwd(), AA_SIM_RESULTS_FILE=NULL,
tutorial_consistency_set)),4)
# Unfortunately can't test the filepath method on travis, but can locally
skip_on_travis()
skip_on_cran()
#expect_equal(nrow(read_simulation_results("/home/kja505/Dropbox/spartan_web_data/Technique1/AA_Spartan2/CSV_Structure/",
# AA_SIM_RESULTS_FILE = "AA_SimResponses.csv", AA_SIM_RESULTS_OBJECT = NULL)),9060)
#expect_equal(ncol(read_simulation_results("/home/kja505/Dropbox/spartan_web_data/Technique1/AA_Spartan2/CSV_Structure/",
# AA_SIM_RESULTS_FILE = "AA_SimResponses.csv", AA_SIM_RESULTS_OBJECT = NULL)),4)
})
test_that("retrieve_results_for_comparison_result_set", {
# This should return one set of results, to which everything else is compared
data("tutorial_consistency_set")
expect_equal(nrow(retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)),1)
# Check sample size is 1 and set is 1
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)[,1],1)
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)[,2],1)
# Check for sample size of 50
expect_equal(nrow(retrieve_results_for_comparison_result_set(tutorial_consistency_set,50)),50)
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,50)[1,1],50)
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,50)[1,2],1)
})
test_that("compare_result_sets_to_comparison_set", {
data("tutorial_consistency_set")
set1<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)
results <- compare_result_sets_to_comparison_set(20, tutorial_consistency_set, set1, 1, c("Velocity","Displacement"))
# results should in this case have 19 rows and 6 columns
expect_equal(nrow(results),19)
expect_equal(ncol(results),6)
# Now we can check a few known results
expect_equal(round(results[6,3][[1]],1),0.0)
expect_equal(round(results[6,6][[1]],1),1.0)
# Do the same for sample size of 300
set300<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,300)
results <- compare_result_sets_to_comparison_set(20, tutorial_consistency_set, set300, 300, c("Velocity","Displacement"))
expect_equal(nrow(results),19)
expect_equal(ncol(results),6)
# From previous analyses we know that sample size 300 set 4 velocity A-Test was 0.5138565, and displacement 0.5559835
# Note the rounding to 7 dp to make sure the floating point doesnt cause an issue
expect_equal(round(results[4,3][[1]],7),0.5138565)
expect_equal(round(results[4,5][[1]],7),0.5559835)
})
test_that("generate_a_test_score", {
# Going to give the function some distributions and check the score is calculated correctly
data("tutorial_consistency_set")
set1<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)
COMPAREDSET <- subset_results_by_param_value_set(c("SampleSize", "Set"),
tutorial_consistency_set,
c(1, 2))
expect_equal(as.numeric(generate_a_test_score(NULL, set1, COMPAREDSET, c("Velocity","Displacement"))),c(0,1,0,1))
# Now a different row, for sample 300
set300<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,300)
COMPAREDSET <- subset_results_by_param_value_set(c("SampleSize", "Set"),
tutorial_consistency_set,
c(300, 18))
expect_equal(round(as.numeric(generate_a_test_score(NULL, set300, COMPAREDSET, c("Velocity","Displacement"))),7),c(0.5421483,0.5421483,0.5770353,0.5770353))
})
test_that("graph_sample_size_results", {
# Difficult one to test - going to test that some output is produced
SET1 <- retrieve_results_for_comparison_result_set(tutorial_consistency_set, 1)
# Now do the comparisons for all other sets for this sample size
SIZE_RESULTS <- compare_result_sets_to_comparison_set(
20, tutorial_consistency_set, SET1, 1, c("Velocity","Displacement"))
# Plot
graph_sample_size_results(getwd(), NULL, 1, SIZE_RESULTS,
c("Velocity","Displacement"), 0.23)
expect_true(file.exists(file.path(getwd(),"/1Samples.pdf")))
file.remove(file.path(getwd(),"/1Samples.pdf"))
SET300 <- retrieve_results_for_comparison_result_set(tutorial_consistency_set, 1)
SIZE_RESULTS <- compare_result_sets_to_comparison_set(
20, tutorial_consistency_set, SET300, 300, c("Velocity","Displacement"))
# Plot
graph_sample_size_results(getwd(), NULL, 300, SIZE_RESULTS,
c("Velocity","Displacement"), 0.23)
expect_true(file.exists(file.path(getwd(),"/300Samples.pdf")))
file.remove(file.path(getwd(),"/300Samples.pdf"))
})
test_that("generate_scores_for_all_sample_sizes", {
# The above functions are all called within this one, so we just need to make sure this produces some output
data("tutorial_consistency_set")
analysis_result <- generate_scores_for_all_sample_sizes(
getwd(), c(1,50), tutorial_consistency_set, 20, c("Velocity","Displacement"),
0.23, NULL)
# Analysis result should have 19 comparisons per set, so 38 rows
expect_equal(nrow(analysis_result),38)
expect_equal(ncol(analysis_result),6)
expect_true(file.exists(file.path(getwd(),"/50Samples.pdf")))
expect_true(file.exists(file.path(getwd(),"/1Samples.pdf")))
file.remove(file.path(getwd(),"/1Samples.pdf"))
file.remove(file.path(getwd(),"/50Samples.pdf"))
})
test_that("read_model_result_file", {
# Make a result file:
result <- cbind(4.4,86.5922532761)
result <- rbind(result, cbind(4.7621290718,37.8444574614))
result <- rbind(result, cbind(3.5112582903,52.5471704902))
write.csv(result,file=file.path(getwd(),"Test_Results_CSV_file.csv"),row.names=F,quote=F)
# Test a result file is read in correctly
read_result <- read_model_result_file(getwd(), "Test_Results_CSV_file.csv",
outputfilecolstart = 1,
outputfilecolend = 2)
expect_equal(ncol(read_result),2)
expect_equal(nrow(read_result),3)
# What if we just read the second column, calls a different part of the function:
read_result <- read_model_result_file(getwd(), "Test_Results_CSV_file.csv",
outputfilecolstart = 2,
outputfilecolend = 2)
expect_equal(ncol(read_result),1)
expect_equal(nrow(read_result),3)
file.remove(file.path(getwd(),"Test_Results_CSV_file.csv"))
})
test_that("import_model_result", {
# Make a result file:
result <- cbind(4.4,86.5922532761)
result <- rbind(result, cbind(4.7621290718,37.8444574614))
result <- rbind(result, cbind(3.5112582903,52.5471704902))
write.csv(result,file=file.path(getwd(),"Test_Results_CSV_file.csv"),row.names=F,quote=F)
sim_result <- import_model_result(getwd(), "Test_Results_CSV_file.csv",
altfilename=NULL, outputfilecolstart = 1,
outputfilecolend = 2)
# Check the structure of the result
expect_equal(ncol(sim_result),2)
expect_equal(nrow(sim_result),3)
# Now what if the result had 0 entries, and we read an alternative:
result <- data.frame(matrix(vector(), 0, 2,
dimnames=list(c(), c("Velocity", "Displacement"))),
stringsAsFactors=F)
write.csv(result,file=file.path(getwd(),"Test_Results_CSV_file2.csv"),row.names=F,quote=F)
result <- cbind(4.4,86.5922532761)
result <- rbind(result, cbind(3.5112582903,52.5471704902))
write.csv(result,file=file.path(getwd(),"Test_Results_Alternative_file.csv"),row.names=F,quote=F)
sim_result <- import_model_result(getwd(), "Test_Results_CSV_file2.csv",
"Test_Results_Alternative_file.csv", outputfilecolstart = 1,
outputfilecolend = 2)
# Sim_result should contain the alternative results
expect_equal(ncol(sim_result),2)
expect_equal(nrow(sim_result),2)
file.remove(file.path(getwd(),"Test_Results_CSV_file2.csv"))
file.remove(file.path(getwd(),"Test_Results_CSV_file.csv"))
file.remove(file.path(getwd(),"Test_Results_Alternative_file.csv"))
})
test_that("append_time_to_argument", {
expect_equal(append_time_to_argument("ResultFile.csv", 12, "csv"),"ResultFile_12.csv")
})
test_that("produce_atest_score_summary", {
# Read in the exemplar test data
data("a_test_results")
allSubset_ATest_Scores<-read_simulation_results(getwd(),NULL,a_test_results)
# Produce the a-test summary, using the package exemplar data
atest_summary <- produce_atest_score_summary(
c(1,5,50,100,300), allSubset_ATest_Scores, c("Velocity","Displacement"))
# Check the structure
expect_equal(nrow(atest_summary), 5)
expect_equal(ncol(atest_summary), 5)
})
test_that("generate_headers_for_atest_file", {
# Simple test to make sure the headers are generated correctly
a<-cbind(1,0.9,0.9,0.6,0.6)
colnames(a)<-generate_headers_for_atest_file(c("Velocity","Displacement"))
expect_equal(toString(colnames(a)),
"samplesize, VelocityMaxA, VelocityMedianA, DisplacementMaxA, DisplacementMedianA")
})
test_that("aa_getATestResults", {
# Overarching call to many of the above methods
# Inputs are checked, we just need to check CSV is output and the results returned
data("tutorial_consistency_set")
scores <- aa_getATestResults(getwd(), c(1,50), 20, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = NULL,
AA_SIM_RESULTS_OBJECT = tutorial_consistency_set,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
expect_equal(nrow(scores),38)
expect_equal(ncol(scores),6)
expect_true(file.exists(file.path(getwd(),"AA_A_Test_Summary.csv")))
expect_true(file.exists(file.path(getwd(),"/50Samples.pdf")))
expect_true(file.exists(file.path(getwd(),"/1Samples.pdf")))
file.remove(file.path(getwd(),"AA_A_Test_Summary.csv"))
file.remove(file.path(getwd(),"/1Samples.pdf"))
file.remove(file.path(getwd(),"/50Samples.pdf"))
# By file:
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"), "ATests.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv", AA_SIM_RESULTS_OBJECT = NULL)
expect_true(file.exists(file.path(getwd(),"AA","ATests.csv")))
expect_true(file.exists(file.path(getwd(),"AA","2Samples.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","1Samples.pdf")))
unlink(file.path(getwd(),"AA"),recursive = TRUE)
})
test_that("aa_getATestResults_overTime", {
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2, TIMEPOINTS=c(12,36), TIMEPOINTSCALE="Hours")
aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"), "ATests.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv", AA_SIM_RESULTS_OBJECT = NULL,TIMEPOINTS=c(12,36), TIMEPOINTSCALE="Hours")
expect_true(file.exists(file.path(getwd(),"AA","ATests_12.csv")))
expect_true(file.exists(file.path(getwd(),"AA","ATests_36.csv")))
expect_true(file.exists(file.path(getwd(),"AA","2Samples_12.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","1Samples_12.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","2Samples_36.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","1Samples_36.pdf")))
unlink(file.path(getwd(),"AA"),recursive = TRUE)
})
test_that("aa_summariseReplicateRuns", {
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
expect_true(file.exists(file.path(getwd(),"AA","AA_Summary.csv")))
# Check structure
result<-read_from_csv(file.path(getwd(),"AA","AA_Summary.csv"))
expect_true(ncol(result)==4)
expect_true(nrow(result)==6)
expect_true(nrow(subset(result, result$SampleSize==1))==2)
expect_true(nrow(subset(result, result$SampleSize==2))==4)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_summariseReplicateRuns_overTime", {
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2,TIMEPOINTS=c(12,36),
TIMEPOINTSCALE="Hours")
expect_true(file.exists(file.path(getwd(),"AA","AA_Summary_12.csv")))
# Check structure
result<-read_from_csv(file.path(getwd(),"AA","AA_Summary_12.csv"))
expect_true(ncol(result)==4)
expect_true(nrow(result)==6)
expect_true(nrow(subset(result, result$SampleSize==1))==2)
expect_true(nrow(subset(result, result$SampleSize==2))==4)
expect_false(any(is.na(result)))
expect_true(file.exists(file.path(getwd(),"AA","AA_Summary_36.csv")))
# Check structure
result<-read_from_csv(file.path(getwd(),"AA","AA_Summary_36.csv"))
expect_true(ncol(result)==4)
expect_true(nrow(result)==6)
expect_true(nrow(subset(result, result$SampleSize==1))==2)
expect_true(nrow(subset(result, result$SampleSize==2))==4)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_sampleSizeSummary", {
# File:
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = NULL, TIMEPOINTSCALE = NULL)
expect_true(file.exists(file.path(getwd(),"AA","Sample_Summary.csv")))
# Check structure:
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
# Object
dir.create(file.path(getwd(),"AA"))
data("tutorial_consistency_set")
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = NULL,
AA_SIM_RESULTS_OBJECT = tutorial_consistency_set,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = NULL,
ATESTRESULTS_OBJECT = scores, TIMEPOINTS = NULL, TIMEPOINTSCALE = NULL)
expect_true(file.exists(file.path(getwd(),"AA","Sample_Summary.csv")))
# Check structure:
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_sampleSizeSummary_overTime", {
# By File:
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2,TIMEPOINTS=c(12,36),
TIMEPOINTSCALE="Hours")
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = c(12,36),
TIMEPOINTSCALE = "Hours")
aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = c(12,36), TIMEPOINTSCALE = "Hours")
expect_true(file.exists(file.path(getwd(),"AA","Sample_Summary_12.csv")))
# Check structure:
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary_12.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary_36.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
# By Object isn't available yet
})
test_that("get_medians_for_size_subsets", {
setup_aleatory_analysis_tests()
subset_medians <- get_medians_for_size_subsets(file.path(getwd(),"AA"), 2,
1, c("Velocity","Displacement"), "trackedCells_Close.csv",
ALTFILENAME=NULL, 10, 11)
expect_true(nrow(subset_medians)==2)
expect_true(ncol(subset_medians)==4)
expect_false(any(is.na(subset_medians)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_graphSampleSizeSummary", {
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
results <- aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = NULL, TIMEPOINTSCALE = NULL)
aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = NULL,
SAMPLESUMMARY_FILE = "Sample_Summary.csv")
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes.pdf")))
expect_message(aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = NULL,
SAMPLESUMMARY_FILE = "Sample_Summary_Wrong.csv"), "Specified A-Test Summary File does not exist")
# By object:
aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = results,
SAMPLESUMMARY_FILE = NULL)
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes.pdf")))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
# Timepoints:
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2,TIMEPOINTS=c(12,36),
TIMEPOINTSCALE="Hours")
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = c(12,36),
TIMEPOINTSCALE = "Hours")
results <- aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = c(12,36), TIMEPOINTSCALE = "Hours")
aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = NULL,
SAMPLESUMMARY_FILE = "Sample_Summary.csv",TIMEPOINTS=c(12,36), TIMEPOINTSCALE="Hours")
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes_12.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes_36.pdf")))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
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library(spartan)
context("Test of Spartan Consistency Analysis")
test_that("read_simulation_results", {
# Simple function that reads in the simulation results from a CSV or R object
# The existance of the paths had been checked by pre-execution tests
# The R object is part of the package
data("tutorial_consistency_set")
expect_equal(nrow(read_simulation_results(getwd(), AA_SIM_RESULTS_FILE=NULL,
tutorial_consistency_set)),9060)
expect_equal(ncol(read_simulation_results(getwd(), AA_SIM_RESULTS_FILE=NULL,
tutorial_consistency_set)),4)
# Unfortunately can't test the filepath method on travis, but can locally
skip_on_travis()
skip_on_cran()
#expect_equal(nrow(read_simulation_results("/home/kja505/Dropbox/spartan_web_data/Technique1/AA_Spartan2/CSV_Structure/",
# AA_SIM_RESULTS_FILE = "AA_SimResponses.csv", AA_SIM_RESULTS_OBJECT = NULL)),9060)
#expect_equal(ncol(read_simulation_results("/home/kja505/Dropbox/spartan_web_data/Technique1/AA_Spartan2/CSV_Structure/",
# AA_SIM_RESULTS_FILE = "AA_SimResponses.csv", AA_SIM_RESULTS_OBJECT = NULL)),4)
})
test_that("retrieve_results_for_comparison_result_set", {
# This should return one set of results, to which everything else is compared
data("tutorial_consistency_set")
expect_equal(nrow(retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)),1)
# Check sample size is 1 and set is 1
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)[,1],1)
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)[,2],1)
# Check for sample size of 50
expect_equal(nrow(retrieve_results_for_comparison_result_set(tutorial_consistency_set,50)),50)
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,50)[1,1],50)
expect_equal(retrieve_results_for_comparison_result_set(tutorial_consistency_set,50)[1,2],1)
})
test_that("compare_result_sets_to_comparison_set", {
data("tutorial_consistency_set")
set1<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)
results <- compare_result_sets_to_comparison_set(20, tutorial_consistency_set, set1, 1, c("Velocity","Displacement"))
# results should in this case have 19 rows and 6 columns
expect_equal(nrow(results),19)
expect_equal(ncol(results),6)
# Now we can check a few known results
expect_equal(round(results[6,3][[1]],1),0.0)
expect_equal(round(results[6,6][[1]],1),1.0)
# Do the same for sample size of 300
set300<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,300)
results <- compare_result_sets_to_comparison_set(20, tutorial_consistency_set, set300, 300, c("Velocity","Displacement"))
expect_equal(nrow(results),19)
expect_equal(ncol(results),6)
# From previous analyses we know that sample size 300 set 4 velocity A-Test was 0.5138565, and displacement 0.5559835
# Note the rounding to 7 dp to make sure the floating point doesnt cause an issue
expect_equal(round(results[4,3][[1]],7),0.5138565)
expect_equal(round(results[4,5][[1]],7),0.5559835)
})
test_that("generate_a_test_score", {
# Going to give the function some distributions and check the score is calculated correctly
data("tutorial_consistency_set")
set1<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,1)
COMPAREDSET <- subset_results_by_param_value_set(c("SampleSize", "Set"),
tutorial_consistency_set,
c(1, 2))
expect_equal(as.numeric(generate_a_test_score(NULL, set1, COMPAREDSET, c("Velocity","Displacement"))),c(0,1,0,1))
# Now a different row, for sample 300
set300<-retrieve_results_for_comparison_result_set(tutorial_consistency_set,300)
COMPAREDSET <- subset_results_by_param_value_set(c("SampleSize", "Set"),
tutorial_consistency_set,
c(300, 18))
expect_equal(round(as.numeric(generate_a_test_score(NULL, set300, COMPAREDSET, c("Velocity","Displacement"))),7),c(0.5421483,0.5421483,0.5770353,0.5770353))
})
test_that("graph_sample_size_results", {
# Difficult one to test - going to test that some output is produced
SET1 <- retrieve_results_for_comparison_result_set(tutorial_consistency_set, 1)
# Now do the comparisons for all other sets for this sample size
SIZE_RESULTS <- compare_result_sets_to_comparison_set(
20, tutorial_consistency_set, SET1, 1, c("Velocity","Displacement"))
# Plot
graph_sample_size_results(getwd(), NULL, 1, SIZE_RESULTS,
c("Velocity","Displacement"), 0.23)
expect_true(file.exists(file.path(getwd(),"/1Samples.pdf")))
file.remove(file.path(getwd(),"/1Samples.pdf"))
SET300 <- retrieve_results_for_comparison_result_set(tutorial_consistency_set, 1)
SIZE_RESULTS <- compare_result_sets_to_comparison_set(
20, tutorial_consistency_set, SET300, 300, c("Velocity","Displacement"))
# Plot
graph_sample_size_results(getwd(), NULL, 300, SIZE_RESULTS,
c("Velocity","Displacement"), 0.23)
expect_true(file.exists(file.path(getwd(),"/300Samples.pdf")))
file.remove(file.path(getwd(),"/300Samples.pdf"))
})
test_that("generate_scores_for_all_sample_sizes", {
# The above functions are all called within this one, so we just need to make sure this produces some output
data("tutorial_consistency_set")
analysis_result <- generate_scores_for_all_sample_sizes(
getwd(), c(1,50), tutorial_consistency_set, 20, c("Velocity","Displacement"),
0.23, NULL)
# Analysis result should have 19 comparisons per set, so 38 rows
expect_equal(nrow(analysis_result),38)
expect_equal(ncol(analysis_result),6)
expect_true(file.exists(file.path(getwd(),"/50Samples.pdf")))
expect_true(file.exists(file.path(getwd(),"/1Samples.pdf")))
file.remove(file.path(getwd(),"/1Samples.pdf"))
file.remove(file.path(getwd(),"/50Samples.pdf"))
})
test_that("read_model_result_file", {
# Make a result file:
result <- cbind(4.4,86.5922532761)
result <- rbind(result, cbind(4.7621290718,37.8444574614))
result <- rbind(result, cbind(3.5112582903,52.5471704902))
write.csv(result,file=file.path(getwd(),"Test_Results_CSV_file.csv"),row.names=F,quote=F)
# Test a result file is read in correctly
read_result <- read_model_result_file(getwd(), "Test_Results_CSV_file.csv",
outputfilecolstart = 1,
outputfilecolend = 2)
expect_equal(ncol(read_result),2)
expect_equal(nrow(read_result),3)
# What if we just read the second column, calls a different part of the function:
read_result <- read_model_result_file(getwd(), "Test_Results_CSV_file.csv",
outputfilecolstart = 2,
outputfilecolend = 2)
expect_equal(ncol(read_result),1)
expect_equal(nrow(read_result),3)
file.remove(file.path(getwd(),"Test_Results_CSV_file.csv"))
})
test_that("import_model_result", {
# Make a result file:
result <- cbind(4.4,86.5922532761)
result <- rbind(result, cbind(4.7621290718,37.8444574614))
result <- rbind(result, cbind(3.5112582903,52.5471704902))
write.csv(result,file=file.path(getwd(),"Test_Results_CSV_file.csv"),row.names=F,quote=F)
sim_result <- import_model_result(getwd(), "Test_Results_CSV_file.csv",
altfilename=NULL, outputfilecolstart = 1,
outputfilecolend = 2)
# Check the structure of the result
expect_equal(ncol(sim_result),2)
expect_equal(nrow(sim_result),3)
# Now what if the result had 0 entries, and we read an alternative:
result <- data.frame(matrix(vector(), 0, 2,
dimnames=list(c(), c("Velocity", "Displacement"))),
stringsAsFactors=F)
write.csv(result,file=file.path(getwd(),"Test_Results_CSV_file2.csv"),row.names=F,quote=F)
result <- cbind(4.4,86.5922532761)
result <- rbind(result, cbind(3.5112582903,52.5471704902))
write.csv(result,file=file.path(getwd(),"Test_Results_Alternative_file.csv"),row.names=F,quote=F)
sim_result <- import_model_result(getwd(), "Test_Results_CSV_file2.csv",
"Test_Results_Alternative_file.csv", outputfilecolstart = 1,
outputfilecolend = 2)
# Sim_result should contain the alternative results
expect_equal(ncol(sim_result),2)
expect_equal(nrow(sim_result),2)
file.remove(file.path(getwd(),"Test_Results_CSV_file2.csv"))
file.remove(file.path(getwd(),"Test_Results_CSV_file.csv"))
file.remove(file.path(getwd(),"Test_Results_Alternative_file.csv"))
})
test_that("append_time_to_argument", {
expect_equal(append_time_to_argument("ResultFile.csv", 12, "csv"),"ResultFile_12.csv")
})
test_that("produce_atest_score_summary", {
# Read in the exemplar test data
data("a_test_results")
allSubset_ATest_Scores<-read_simulation_results(getwd(),NULL,a_test_results)
# Produce the a-test summary, using the package exemplar data
atest_summary <- produce_atest_score_summary(
c(1,5,50,100,300), allSubset_ATest_Scores, c("Velocity","Displacement"))
# Check the structure
expect_equal(nrow(atest_summary), 5)
expect_equal(ncol(atest_summary), 5)
})
test_that("generate_headers_for_atest_file", {
# Simple test to make sure the headers are generated correctly
a<-cbind(1,0.9,0.9,0.6,0.6)
colnames(a)<-generate_headers_for_atest_file(c("Velocity","Displacement"))
expect_equal(toString(colnames(a)),
"samplesize, VelocityMaxA, VelocityMedianA, DisplacementMaxA, DisplacementMedianA")
})
test_that("aa_getATestResults", {
# Overarching call to many of the above methods
# Inputs are checked, we just need to check CSV is output and the results returned
data("tutorial_consistency_set")
scores <- aa_getATestResults(getwd(), c(1,50), 20, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = NULL,
AA_SIM_RESULTS_OBJECT = tutorial_consistency_set,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
expect_equal(nrow(scores),38)
expect_equal(ncol(scores),6)
expect_true(file.exists(file.path(getwd(),"AA_A_Test_Summary.csv")))
expect_true(file.exists(file.path(getwd(),"/50Samples.pdf")))
expect_true(file.exists(file.path(getwd(),"/1Samples.pdf")))
file.remove(file.path(getwd(),"AA_A_Test_Summary.csv"))
file.remove(file.path(getwd(),"/1Samples.pdf"))
file.remove(file.path(getwd(),"/50Samples.pdf"))
# By file:
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"), "ATests.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv", AA_SIM_RESULTS_OBJECT = NULL)
expect_true(file.exists(file.path(getwd(),"AA","ATests.csv")))
expect_true(file.exists(file.path(getwd(),"AA","2Samples.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","1Samples.pdf")))
unlink(file.path(getwd(),"AA"),recursive = TRUE)
})
test_that("aa_getATestResults_overTime", {
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2, TIMEPOINTS=c(12,36), TIMEPOINTSCALE="Hours")
aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"), "ATests.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv", AA_SIM_RESULTS_OBJECT = NULL,TIMEPOINTS=c(12,36), TIMEPOINTSCALE="Hours")
expect_true(file.exists(file.path(getwd(),"AA","ATests_12.csv")))
expect_true(file.exists(file.path(getwd(),"AA","ATests_36.csv")))
expect_true(file.exists(file.path(getwd(),"AA","2Samples_12.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","1Samples_12.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","2Samples_36.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","1Samples_36.pdf")))
unlink(file.path(getwd(),"AA"),recursive = TRUE)
})
test_that("aa_summariseReplicateRuns", {
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
expect_true(file.exists(file.path(getwd(),"AA","AA_Summary.csv")))
# Check structure
result<-read_from_csv(file.path(getwd(),"AA","AA_Summary.csv"))
expect_true(ncol(result)==4)
expect_true(nrow(result)==6)
expect_true(nrow(subset(result, result$SampleSize==1))==2)
expect_true(nrow(subset(result, result$SampleSize==2))==4)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_summariseReplicateRuns_overTime", {
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2,TIMEPOINTS=c(12,36),
TIMEPOINTSCALE="Hours")
expect_true(file.exists(file.path(getwd(),"AA","AA_Summary_12.csv")))
# Check structure
result<-read_from_csv(file.path(getwd(),"AA","AA_Summary_12.csv"))
expect_true(ncol(result)==4)
expect_true(nrow(result)==6)
expect_true(nrow(subset(result, result$SampleSize==1))==2)
expect_true(nrow(subset(result, result$SampleSize==2))==4)
expect_false(any(is.na(result)))
expect_true(file.exists(file.path(getwd(),"AA","AA_Summary_36.csv")))
# Check structure
result<-read_from_csv(file.path(getwd(),"AA","AA_Summary_36.csv"))
expect_true(ncol(result)==4)
expect_true(nrow(result)==6)
expect_true(nrow(subset(result, result$SampleSize==1))==2)
expect_true(nrow(subset(result, result$SampleSize==2))==4)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_sampleSizeSummary", {
# File:
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = NULL, TIMEPOINTSCALE = NULL)
expect_true(file.exists(file.path(getwd(),"AA","Sample_Summary.csv")))
# Check structure:
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
# Object
dir.create(file.path(getwd(),"AA"))
data("tutorial_consistency_set")
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = NULL,
AA_SIM_RESULTS_OBJECT = tutorial_consistency_set,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = NULL,
ATESTRESULTS_OBJECT = scores, TIMEPOINTS = NULL, TIMEPOINTSCALE = NULL)
expect_true(file.exists(file.path(getwd(),"AA","Sample_Summary.csv")))
# Check structure:
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_sampleSizeSummary_overTime", {
# By File:
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2,TIMEPOINTS=c(12,36),
TIMEPOINTSCALE="Hours")
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = c(12,36),
TIMEPOINTSCALE = "Hours")
aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = c(12,36), TIMEPOINTSCALE = "Hours")
expect_true(file.exists(file.path(getwd(),"AA","Sample_Summary_12.csv")))
# Check structure:
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary_12.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
result<-read_from_csv(file.path(getwd(),"AA","Sample_Summary_36.csv"))
expect_true(nrow(result)==2)
expect_true(ncol(result)==5)
expect_false(any(is.na(result)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
# By Object isn't available yet
})
test_that("get_medians_for_size_subsets", {
setup_aleatory_analysis_tests()
subset_medians <- get_medians_for_size_subsets(file.path(getwd(),"AA"), 2,
1, c("Velocity","Displacement"), "trackedCells_Close.csv",
ALTFILENAME=NULL, 10, 11)
expect_true(nrow(subset_medians)==2)
expect_true(ncol(subset_medians)==4)
expect_false(any(is.na(subset_medians)))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
test_that("aa_graphSampleSizeSummary", {
setup_aleatory_analysis_tests()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2)
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = NULL,
TIMEPOINTSCALE = NULL, GRAPHNAME = NULL)
results <- aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = NULL, TIMEPOINTSCALE = NULL)
aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = NULL,
SAMPLESUMMARY_FILE = "Sample_Summary.csv")
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes.pdf")))
expect_message(aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = NULL,
SAMPLESUMMARY_FILE = "Sample_Summary_Wrong.csv"), "Specified A-Test Summary File does not exist")
# By object:
aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = results,
SAMPLESUMMARY_FILE = NULL)
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes.pdf")))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
# Timepoints:
setup_aleatory_analysis_overTime()
aa_summariseReplicateRuns(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"),
"trackedCells_Close.csv", ALTFILENAME = NULL,
10,11,"AA_Summary.csv",NUMSUBSETSPERSAMPLESIZE=2,TIMEPOINTS=c(12,36),
TIMEPOINTSCALE="Hours")
scores <- aa_getATestResults(file.path(getwd(),"AA"), c(1,2), 2, c("Velocity","Displacement"),
"AA_A_Test_Summary.csv", 0.23,
AA_SIM_RESULTS_FILE = "AA_Summary.csv",
AA_SIM_RESULTS_OBJECT = NULL,
TIMEPOINTS = c(12,36),
TIMEPOINTSCALE = "Hours")
results <- aa_sampleSizeSummary(file.path(getwd(),"AA"), c(1,2), c("Velocity","Displacement"), "Sample_Summary.csv", ATESTRESULTS_FILE = "AA_A_Test_Summary.csv",
ATESTRESULTS_OBJECT = NULL, TIMEPOINTS = c(12,36), TIMEPOINTSCALE = "Hours")
aa_graphSampleSizeSummary(file.path(getwd(),"AA"), c("Velocity","Displacement"), 2, 0.56,
0.66, 0.73, "AA_ATestMaxes.pdf",
SAMPLESUMMARY_OBJECT = NULL,
SAMPLESUMMARY_FILE = "Sample_Summary.csv",TIMEPOINTS=c(12,36), TIMEPOINTSCALE="Hours")
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes_12.pdf")))
expect_true(file.exists(file.path(getwd(),"AA","AA_ATestMaxes_36.pdf")))
unlink(file.path(getwd(),"AA"),recursive=TRUE)
})
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