def test_evalformat(self):
ex = Experiment()
ex.addOutputFile(test_out)
ex.collectData()
for (index, indexsplit) in ((index, indexsplit) for index in eval_index
for indexsplit in eval_indexsplit):
for (cols, fgs) in ((cols, fgs) for cols in self.test_cols
for fgs in self.test_fgs):
ev = IPETEvaluation(index=index, indexsplit=indexsplit)
if cols is not None:
for (ind_c, ind_a) in cols:
col = IPETEvaluationColumn(**columns[ind_c])
if col_filters[ind_c] is not None:
colfilter = IPETFilter(**col_filters[ind_c])
col.addFilter(colfilter)
if ind_a:
agg = Aggregation(**aggs[ind_c])
col.addAggregation(agg)
ev.addColumn(col)
if fgs is not None:
for ind in fgs:
fg = IPETFilterGroup(**filtergroups[ind])
if filters[ind] is not None:
fgfilter = IPETFilter(**filters[ind])
fg.addFilter(fgfilter)
ev.addFilterGroup(fg)
try:
tab_long, tab_agg = ev.evaluate(ex)
except AttributeError as e:
self.assertTrue((
cols is None or fgs is None
), "Either the number of columns or the number of filtergroups should be 0."
)
continue
self.assertEqual(type(tab_long), pd.DataFrame,
"Type of long table wrong.")
self.assertEqual(type(tab_agg), pd.DataFrame,
"Type of aggregated table wrong.")
# do not allow rowindex to be empty
if indexsplit == 0:
indexsplit = 1
rowindex_level = len(index.split()[:indexsplit])
columns_level = len(index.split()[indexsplit:])
self.assertEqual(tab_long.columns.nlevels, columns_level + 1,
"Level of columnindex of long table wrong.")
self.assertEqual(tab_agg.index.nlevels, columns_level + 1,
"Level of rowindex of agg table wrong.")
self.assertEqual(tab_long.index.nlevels, rowindex_level,
"Level of rowindex of long table wrong.")
def setUp(self):
"""Set up the evaluation
The evaluation has only one column
for the 'Data' key, but applies
filtering on the 'FilterData' key
"""
self.ex = HelperExperiment()
ev = IPETEvaluation(index="Index", indexsplit="1")
ev.addColumn(IPETEvaluationColumn(origcolname="Data"))
fg = IPETFilterGroup("TestGroup")
fi = IPETFilter(expression1="FilterData",
expression2=-1.0,
operator="eq")
fg.addFilter(fi)
ev.addFilterGroup(fg)
self.ev = ev
def setUp(self):
self.ex = HelpExperiment()
ev = IPETEvaluation(defaultgroup="C1",
index="A C"
)
ev.addColumn(IPETEvaluationColumn(origcolname="D"))
fg = IPETFilterGroup("Test")
fi = IPETFilter(operator="keep", datakey="A")
fi.addChild(IPETValue("A1"))
fg.addFilter(fi)
fg2 = IPETFilterGroup("Test2")
fi2 = IPETFilter(operator="keep", datakey="A")
fi2.addChild(IPETValue("A2"))
fg2.addFilter(fi2)
ev.addFilterGroup(fg2)
ev.addFilterGroup(fg)
self.ev = ev
self.fg = fg
def setUp(self):
"""
create an evaluation around a helper experiment
"""
self.ex = HelpExperiment()
ev = IPETEvaluation(defaultgroup="C1",
index="A B C",
fillin=True,
indexsplit=3)
ev.addColumn(
IPETEvaluationColumn(origcolname="D", alternative="100",
maxval=15))
fg = IPETFilterGroup("Test")
fi = IPETFilter(operator="keep", datakey="A")
fi.addChild(IPETValue("A1"))
fg.addFilter(fi)
ev.addFilterGroup(fg)
self.ev = ev
self.fg = fg
def createAndEvaluateColumn(self, col):
ev = IPETEvaluation(index="A B C", indexsplit=3)
ev.addColumn(col)
ev.evaluate(self.helper)
return ev