def test_io():
refdm = DataMatrix(length=3)
refdm[u'tést'] = 1, 2, u''
refdm.B = u'mathôt', u'b', u'x'
refdm.C = u'a,\\b"\'c', 8, u''
testdm = io.readtxt('testcases/data/data.csv')
check_dm(refdm, testdm)
io.writetxt(testdm, 'tmp.csv')
testdm = io.readtxt('tmp.csv')
check_dm(refdm, testdm)
refdm = io.readtxt('testcases/data/line-ending-cr.csv')
check_dm(refdm, testdm)
refdm = io.readtxt('testcases/data/line-ending-crlf.csv')
check_dm(refdm, testdm)
refdm = io.readtxt('testcases/data/data-with-bom.csv')
check_dm(refdm, testdm)
io.writepickle(testdm, 'tmp.pickle')
testdm = io.readpickle('tmp.pickle')
check_dm(refdm, testdm)
io.writexlsx(testdm, 'tmp.xlsx')
with pytest.warns(UserWarning): # Not all rows have column C
testdm = io.readxlsx('tmp.xlsx')
check_dm(refdm, testdm)
io.writexlsx(testdm, 'tmp.xlsx')
with pytest.warns(UserWarning): # Not all rows have column C
testdm = io.readxlsx('tmp.xlsx')
check_dm(refdm, testdm)
def test_io():
refdm = DataMatrix(length=3)
refdm[u'tést'] = 1, 2, u''
refdm.B = u'mathôt', u'b', u'x'
refdm.C = u'a,\\b"\'c', 8, u''
testdm = io.readtxt('testcases/data/data.csv')
check_dm(refdm, testdm)
io.writetxt(testdm, 'tmp.csv')
testdm = io.readtxt('tmp.csv')
check_dm(refdm, testdm)
refdm = io.readtxt('testcases/data/line-ending-cr.csv')
check_dm(refdm, testdm)
refdm = io.readtxt('testcases/data/line-ending-crlf.csv')
check_dm(refdm, testdm)
io.writepickle(testdm, 'tmp.pickle')
testdm = io.readpickle('tmp.pickle')
check_dm(refdm, testdm)
io.writexlsx(testdm, 'tmp.xlsx')
testdm = io.readxlsx('tmp.xlsx')
check_dm(refdm, testdm)
def inner(dm, *arglist, **kwdict):
print("Running with gaze data")
dm = io.readpickle(".cache/gaze-data-%s.pkl" % analysis.exp)
print("All experiments: Keeping only correct trials")
dm = dm.correct == 1
if analysis.exp == "exp2":
print("Exp 2: Keeping only memory trials")
dm = dm.trialType == "memory"
return fnc(dm)
def realdata():
dm = io.readpickle('data/real-data.pkl')
# If the buffered DataMatrix still uses a list-style row index, we convert
# it to the new Index object with this hack.
if isinstance(dm._rowid, list):
from datamatrix._datamatrix._index import Index
object.__setattr__(dm, u'_rowid', Index(dm._rowid))
print(len(dm))
return dm
def test_io():
refdm = DataMatrix(length=3)
refdm[u'tést'] = 1, 2, u''
refdm.B = u'mathôt', u'b', u'x'
refdm.C = u'a,\\b"\'c', 8, u''
testdm = io.readtxt('testcases/data/data.csv')
check_dm(refdm, testdm)
io.writetxt(testdm, 'tmp.csv')
testdm = io.readtxt('tmp.csv')
check_dm(refdm, testdm)
io.writepickle(testdm, 'tmp.pickle')
testdm = io.readpickle('tmp.pickle')
check_dm(refdm, testdm)
io.writexlsx(testdm, 'tmp.xlsx')
testdm = io.readxlsx('tmp.xlsx')
check_dm(refdm, testdm)
# <markdowncell>
"""
## Data parsing
The data is stored in separate DataMatrix objects, one for each participant,
where each row corresponds to one video fragment. We merge these objects such
that we get a big DataMatrix where each row corresponds to a single
participant, and the cells are averaged across video fragments.
"""
# </markdowncell>
# <codecell>
dm = DataMatrix(length=NSUB)
for row, basename in zip(dm, os.listdir(COR_SRC)):
path = os.path.join(COR_SRC, basename)
sdm = io.readpickle(path)
for colname, col in sdm.columns:
if colname not in dm:
dm[colname] = type(col)
row[colname] = col.mean
# </codecell>
# <markdowncell>
"""
For some analyses it's more convient to have the data in long format such that
each row corresponds to a single voxel. That's what we do here. We also merge
the PRF data into this long format, such that we know the PRF properties for
each voxel.
"""
# </markdowncell>