def test_posixct_in_dataframe_to_pandas(self):
tzone = robjects.vectors.get_timezone()
dt = [datetime(1960, 5, 2),
datetime(1970, 6, 3),
datetime(2012, 7, 1)]
dt = [x.replace(tzinfo=tzone) for x in dt]
# fix the time
ts = [x.timestamp() for x in dt]
# Create an R data.frame with a posixct_vector.
r_dataf = robjects.vectors.DataFrame({
'mydate': robjects.baseenv['as.POSIXct'](
rinterface.FloatSexpVector(ts),
origin=rinterface.StrSexpVector(('1970-01-01',))
)})
# Convert R POSIXct vector to pandas-compatible vector
with localconverter(default_converter + rpyp.converter):
py_dataf = robjects.conversion.rpy2py(r_dataf)
assert pandas.core.dtypes.common.is_datetime64_any_dtype(py_dataf['mydate'])
def __value_py_to_r(value, ri):
"""Returns the R equivalent of a python value"""
val = value.value
if not isinstance(val, (list, tuple)):
# Is this an iterable ?
if hasattr(val,
'__iter__') and not isinstance(val, (str, unicode)):
val = [v for v in val]
else:
# In R scalar values are vectors with one element
# So here we put the scalar value into a list
val = [val]
na_value = None
if value.type == 'bool':
na_value = ri.NALogicalType()
elif value.type == 'byte':
na_value = ri.NAIntegerType(
) # I guess that's correct ? That should probably be tested though
elif value.type == 'float':
na_value = ri.NARealType()
elif value.type == 'int':
na_value = ri.NAIntegerType()
elif value.type == 'string':
na_value = ri.NACharacterType()
# Scan the elements to replace Nones by NA
val = [(na_value if v is None else v) for v in val]
if value.type == 'bool':
return ri.BoolSexpVector(val)
if value.type == 'byte':
return ri.ByteSexpVector(val)
if value.type == 'float':
return ri.FloatSexpVector(val)
if value.type == 'int':
return ri.IntSexpVector(val)
if value.type == 'string':
return ri.StrSexpVector(val)
raise PredictionValueTypeException(value.type)
def test_timeR2Pandas(self):
tzone = rpyp.get_timezone()
dt = [datetime(1960, 5, 2),
datetime(1970, 6, 3),
datetime(2012, 7, 1)]
dt = [x.replace(tzinfo=tzone) for x in dt]
# fix the time
ts = [x.timestamp() for x in dt]
# Create an R POSIXct vector.
r_time = robjects.baseenv['as.POSIXct'](
rinterface.FloatSexpVector(ts),
origin=rinterface.StrSexpVector(('1970-01-01',))
)
# Convert R POSIXct vector to pandas-compatible vector
with localconverter(default_converter + rpyp.converter) as cv:
py_time = robjects.conversion.rpy2py(r_time)
# Check that the round trip did not introduce changes
for expected, obtained in zip(dt, py_time):
assert expected == obtained.to_pydatetime()
def formatForR(params):
kwargs = {}
for key, value in params.iteritems():
if not isinstance(value, list):
value = [value]
t = commonType(value)
if t == float:
v = R.FloatSexpVector(value)
elif t == int:
v = R.IntSexpVector(value)
elif t == bool:
v = R.BoolSexpVector(value)
elif t == str:
v = R.StrSexpVector(value)
else:
v = None
kwargs.update([(key, v)])
return kwargs
def test_init_From_seq_invalid_elt():
seq = (ri.FloatSexpVector([1.0]), lambda x: x,
ri.StrSexpVector(['foo', 'bar']))
with pytest.raises(Exception):
ri.ListSexpVector(seq)
def test_init_from_seq():
seq = (1.0, 2.0, 3.0)
v = ri.FloatSexpVector(seq)
assert len(v) == 3
for x, y in zip(seq, v):
assert x == y
def test_getslice():
vec = ri.FloatSexpVector([1.0, 2.0, 3.0])
vec = vec[0:2]
assert len(vec) == 2
assert vec[0] == 1.0
assert vec[1] == 2.0
def testInitFromIter(self):
it = range(10)
v = ri.FloatSexpVector(it)
self.assertEqual(10, len(v))
for x,y in zip(range(10), v):
self.assertEqual(x, y)
def test_init_From_seq_invalid_float():
seq = (1.0, 'b', 3.0)
with pytest.raises(ValueError):
ri.FloatSexpVector(seq)
def test_init_from_iter():
it = range(3)
v = ri.FloatSexpVector(it)
assert len(v) == 3
for x, y in zip(range(3), v):
assert x == y
def test_NAReal_in_vector():
na_float = ri.NA_Real
x = ri.FloatSexpVector((1.1, na_float, 2.2))
assert math.isnan(x[1])
assert x[0] == 1.1
assert x[2] == 2.2
def npfloat_py2rpy(obj):
return rinterface.FloatSexpVector([obj, ])
# test LBA pdf
acc = VarWaldAccumulator(2, 0.1, 0, 1)
print acc
nsamples = 10000
x = np.linspace(0, 3, nsamples)
pl.plot(x, acc.pdf(x))
pl.xlim(0, 3)
import rpy2.robjects as robjects
r = robjects.r
r['source']('./test/misc/UWA.R')
rx = rinterface.FloatSexpVector(x - .1)
yr = r['digt'](rx, k=2 - .5, l=0, a=.5)
y = np.array(yr)
pl.plot(x, y, color='red')
assert np.any(y - acc.pdf(x) < 1e-10)
#pl.plot( x, acc.cdf(x))
#y=acc.sample(10000, upper_limit=3)
#a=pl.hist(y, 100, normed=True)
#print np.sum(a[1])
#print "PDF Sum: ", (np.sum(acc.pdf(x))/nsamples*10)
#print "PDF Integral: ",scipy.integrate.quad(acc.pdf, 0, np.infty)[0]
#print "CDF(0,1000) :", acc.cdf(np.array([0,1000]))
pl.show()
def _toVector(arg):
return r.FloatSexpVector(arg)
def testNARealInVector(self):
na_float = ri.NARealType()
x = ri.FloatSexpVector((1.1, na_float, 2.2))
self.assertTrue(x[1] is na_float)
self.assertEqual(1.1, x[0])
self.assertEqual(2.2, x[2])
def test_getitem():
vec = ri.FloatSexpVector([1.0, 2.0, 3.0])
assert vec[1] == 2.0
def testInitFromSeq(self):
seq = (1.0, 2.0, 3.0)
v = ri.FloatSexpVector(seq)
self.assertEqual(3, len(v))
for x,y in zip(seq, v):
self.assertEqual(x, y)
def test_setitem():
vec = ri.FloatSexpVector([1.0, 2.0, 3.0])
vec[1] = 100.0
assert vec[1] == 100.0
def testGetSliceFloat(self):
vec = ri.FloatSexpVector([1.0,2.0,3.0])
vec = vec[0:2]
self.assertEqual(2, len(vec))
self.assertEqual(1.0, vec[0])
self.assertEqual(2.0, vec[1])
def __init__(self, obj):
obj = rinterface.FloatSexpVector(obj)
super(FloatVector, self).__init__(obj)