def test_getRowsWithUniqColumn(self):
expectedDict = {'test1': R.IntVector((12,15)), 'test2': R.IntVector((32,12)), 'test3': R.IntVector((3,26))}
expectedSubset = R.DataFrame(expectedDict)
# because the dict from which the dataframe is made is not ordened, the testframe is ordened first to make sure that the column 'test2' is always at the same position
expectedSubset = expectedSubset.rx[True, R.r['with'](expectedSubset, R.r['order'](R.IntVector([rFunctions.index(expectedSubset, 'test1'), rFunctions.index(expectedSubset, 'test2'), rFunctions.index(expectedSubset, 'test3')])),)]
testDict = {'test1': R.IntVector((12,12,15)), 'test2': R.IntVector((32,4,12)), 'test3': R.IntVector((3,12,26))} # note that test1 has 12 in row 1 and row 2
# testing dataframe
testFrame = R.DataFrame(testDict)
# because the dict from which the dataframe is made is not ordened, the testframe is ordened first to make sure that the column 'test2' is always at the same position
testFrame = testFrame.rx[True, R.r['with'](testFrame, R.r['order'](R.IntVector([rFunctions.index(testFrame, 'test1'), rFunctions.index(testFrame, 'test2'), rFunctions.index(testFrame, 'test3')])),)]
actualDfSubset = rFunctions.getRowsWithUniqColumn(testFrame, 'test1') # now only two of the three rows remain, because test1 had 12 twice in the row
# a list to keep track of the results, can't compare two dataframes directly so want to compare their values only
expectedResultList = []
actualResultList = []
# getting the relevant data from both expected and actual subset.
for values in expectedSubset.iteritems():
# and append them to the list
expectedResultList.append([values[0],values[1][0], values[1][1]])
for values in actualDfSubset.iteritems():
actualResultList.append([values[0],values[1][0], values[1][1]])
self.assertEqual(expectedResultList, actualResultList)
def test_getRowsWithUniqColumn(self):
expectedDict = {
'test1': R.IntVector((12, 15)),
'test2': R.IntVector((32, 12)),
'test3': R.IntVector((3, 26))
}
expectedSubset = R.DataFrame(expectedDict)
# because the dict from which the dataframe is made is not ordened, the testframe is ordened first to make sure that the column 'test2' is always at the same position
expectedSubset = expectedSubset.rx[True, R.r['with'](
expectedSubset,
R.r['order'](R.IntVector([
rFunctions.index(expectedSubset, 'test1'),
rFunctions.index(expectedSubset, 'test2'),
rFunctions.index(expectedSubset, 'test3')
])),
)]
testDict = {
'test1': R.IntVector((12, 12, 15)),
'test2': R.IntVector((32, 4, 12)),
'test3': R.IntVector((3, 12, 26))
} # note that test1 has 12 in row 1 and row 2
# testing dataframe
testFrame = R.DataFrame(testDict)
# because the dict from which the dataframe is made is not ordened, the testframe is ordened first to make sure that the column 'test2' is always at the same position
testFrame = testFrame.rx[True, R.r['with'](
testFrame,
R.r['order'](R.IntVector([
rFunctions.index(testFrame, 'test1'),
rFunctions.index(testFrame, 'test2'),
rFunctions.index(testFrame, 'test3')
])),
)]
actualDfSubset = rFunctions.getRowsWithUniqColumn(
testFrame, 'test1'
) # now only two of the three rows remain, because test1 had 12 twice in the row
# a list to keep track of the results, can't compare two dataframes directly so want to compare their values only
expectedResultList = []
actualResultList = []
# getting the relevant data from both expected and actual subset.
for values in expectedSubset.iteritems():
# and append them to the list
expectedResultList.append([values[0], values[1][0], values[1][1]])
for values in actualDfSubset.iteritems():
actualResultList.append([values[0], values[1][0], values[1][1]])
self.assertEqual(expectedResultList, actualResultList)
def test_histogram(self):
###TEST 1 HISTOGRAM###
# if the ouput file already exists, remove it
if os.path.exists(testFolder + 'test_histogram.png'):
os.remove(testFolder + 'test_histogram.png')
# reading in a csv file, seperated by tabs into a matrix
csvData = rFunctions.readCsvFile(testFolder + 'feature.csv',
sep='\t',
head=True,
na='N/A')
# get only the rows with unique ids and put it in a new matrix
csvUniqID = rFunctions.getRowsWithUniqColumn(csvData, 'id')
# get a vector of all intensities using the index function from R_functions
intensityVector = csvData[rFunctions.index(csvUniqID, 'intensity')]
logIntensityVector = rFunctions.takeLog(intensityVector, 10)
# using all possible **kwargs arguments to test if they are all parsed correctly
self.plots.histogram(testFolder + 'test_histogram.png',
logIntensityVector,
plotArgs={'labels': True},
width=400,
height=400,
title='test #features per intensity',
ylab='# of test features')
# if after this the ouput does not exist, fail the test
if not os.path.exists(testFolder + 'test_histogram.png'):
self.fail(
testFolder +
'test_histogram.png does not exist. File not written out correctly'
)
else:
os.remove(testFolder + 'test_histogram.png')
###TEST 3 HISTOGRAMS
# if the test doesn't give an error it succeeded
if os.path.exists(testFolder + 'testOverlapHistogram.png'):
os.remove(
testFolder + 'testOverlapHistogram.png'
) # to make sure that the test isn't passing when the method doesn't work, but the file already exists
outpng = testFolder + 'testOverlapHistogram.png'
vector1 = R.IntVector((0, 2, 2, 3, 3, 3, 4, 4, 5))
vector2 = R.IntVector((2, 4, 4, 5, 5, 5, 6, 6, 7))
vector3 = R.IntVector((4, 6, 6, 7, 7, 7, 8, 8, 9))
plots = rPlots.Plots()
plots.histogram(outpng, vector1, vector2, vector3)
R.r['dev.off']()
if os.path.exists(testFolder + 'testOverlapHistogram.png'):
os.remove(testFolder + 'testOverlapHistogram.png')
def test_boxplotFormulae(self):
# if the ouput file already exists, remove it
if os.path.exists(testFolder + 'boxplotFormulae.png'):
os.remove(testFolder + 'boxplotFormulae.png')
featDataframe = rFunctions.readCsvFile(testFolder + 'feature.csv')
featDataframeUniq = rFunctions.getRowsWithUniqColumn(
featDataframe, 'id')
precursorPerFeatureDataframe = rFunctions.readCsvFile(
testFolder + 'feature_precursor.csv', head=True, sep='\t')
mergedFeatureDataframe = R.r['merge'](featDataframeUniq,
precursorPerFeatureDataframe)
mergedFeatureDataframe[rFunctions.index(
mergedFeatureDataframe,
'intensity')] = R.r['round'](rFunctions.takeLog(
featDataframeUniq[rFunctions.index(featDataframeUniq,
'intensity')], 10))
vector1 = mergedFeatureDataframe[rFunctions.index(
mergedFeatureDataframe, 'X..precursors')]
vector2 = mergedFeatureDataframe[rFunctions.index(
mergedFeatureDataframe, 'intensity')]
self.plots.boxplotFormulae(testFolder + 'boxplotFormulae.png',
vector1,
vector2,
mergedFeatureDataframe,
title='MS/MS per feature per intensity',
ylab='# of MS/MS per feature',
xlab='Rounded log10 of intensity')
R.r['dev.off']()
# if after this the ouput does not exist, fail the test
if not os.path.exists(testFolder + 'boxplotFormulae.png'):
self.fail(
testFolder +
'boxplotFormulae.png does not exist. File not written out correctly'
)
# remove the plot
if os.path.exists(testFolder + 'boxplotFormulae.png'):
os.remove(testFolder + 'boxplotFormulae.png')
def test_boxplotFormulae(self):
# if the ouput file already exists, remove it
if os.path.exists(testFolder+'boxplotFormulae.png'):
os.remove(testFolder+'boxplotFormulae.png')
featDataframe = rFunctions.readCsvFile(testFolder+'feature.csv')
featDataframeUniq = rFunctions.getRowsWithUniqColumn(featDataframe, 'id')
precursorPerFeatureDataframe = rFunctions.readCsvFile(testFolder+'feature_precursor.csv', head=True, sep='\t')
mergedFeatureDataframe = R.r['merge'](featDataframeUniq, precursorPerFeatureDataframe)
mergedFeatureDataframe[rFunctions.index(mergedFeatureDataframe, 'intensity')] = R.r['round'](rFunctions.takeLog(featDataframeUniq[rFunctions.index(featDataframeUniq, 'intensity')], 10))
vector1 = mergedFeatureDataframe[rFunctions.index(mergedFeatureDataframe, 'X..precursors')]
vector2 = mergedFeatureDataframe[rFunctions.index(mergedFeatureDataframe,'intensity')]
self.plots.boxplotFormulae(testFolder+'boxplotFormulae.png', vector1, vector2, mergedFeatureDataframe,
title = 'MS/MS per feature per intensity', ylab = '# of MS/MS per feature', xlab = 'Rounded log10 of intensity')
R.r['dev.off']()
# if after this the ouput does not exist, fail the test
if not os.path.exists(testFolder+'boxplotFormulae.png'):
self.fail(testFolder+'boxplotFormulae.png does not exist. File not written out correctly')
# remove the plot
if os.path.exists(testFolder+'boxplotFormulae.png'):
os.remove(testFolder+'boxplotFormulae.png')
def test_histogram(self):
###TEST 1 HISTOGRAM###
# if the ouput file already exists, remove it
if os.path.exists(testFolder+'test_histogram.png'):
os.remove(testFolder+'test_histogram.png')
# reading in a csv file, seperated by tabs into a matrix
csvData = rFunctions.readCsvFile(testFolder+'feature.csv', sep = '\t', head=True, na='N/A')
# get only the rows with unique ids and put it in a new matrix
csvUniqID = rFunctions.getRowsWithUniqColumn(csvData, 'id')
# get a vector of all intensities using the index function from R_functions
intensityVector = csvData[rFunctions.index(csvUniqID, 'intensity')]
logIntensityVector = rFunctions.takeLog(intensityVector, 10)
# using all possible **kwargs arguments to test if they are all parsed correctly
self.plots.histogram(testFolder+'test_histogram.png', logIntensityVector, plotArgs={'labels':True}, width=400, height=400, title='test #features per intensity', ylab = '# of test features')
# if after this the ouput does not exist, fail the test
if not os.path.exists(testFolder+'test_histogram.png'):
self.fail(testFolder+'test_histogram.png does not exist. File not written out correctly')
else:
os.remove(testFolder+'test_histogram.png')
###TEST 3 HISTOGRAMS
# if the test doesn't give an error it succeeded
if os.path.exists(testFolder+'testOverlapHistogram.png'):
os.remove(testFolder+'testOverlapHistogram.png') # to make sure that the test isn't passing when the method doesn't work, but the file already exists
outpng = testFolder+'testOverlapHistogram.png'
vector1 = R.IntVector((0,2,2,3,3,3,4,4,5))
vector2 = R.IntVector((2,4,4,5,5,5,6,6,7))
vector3 = R.IntVector((4,6,6,7,7,7,8,8,9))
plots = rPlots.Plots()
plots.histogram(outpng,vector1,vector2,vector3)
R.r['dev.off']()
if os.path.exists(testFolder+'testOverlapHistogram.png'):
os.remove(testFolder+'testOverlapHistogram.png')