class _TestNodes(object):
C = varnode()
D = varnode()
@queuenode
def queue_output(cls):
return cls.A() + cls.B()
@nansumnode
def nansum_output(cls):
return cls.A() + cls.sometimes_nan_B()
@cumprodnode
def cumprod_output(cls):
return cls.A() + cls.B()
@evalnode
def sometimes_nan_B(cls):
b = cls.B()
return np.nan if b % 2 else b
@evalnode
def A(cls):
return cls.C() * cls.D()
@evalnode
def B(cls):
accum = 0
while True:
yield accum
accum += 1
@queuenode(size=2)
def queue_size_test(cls):
return 0
#
# check that evalnodes passed as arguments to nodes get
# bound correctly
#
_queue_size = 3
@evalnode
def get_queue_size(cls):
return cls._queue_size
@queuenode(size=get_queue_size)
def queue_size_test2(cls):
return 0
@evalnode
def dataframe(cls):
return pd.DataFrame({"A": range(100)},
index=pd.bdate_range(datetime(1970, 1, 1),
periods=100),
dtype=float)
df_data = datanode(data=dataframe)
df_filter = filternode(data=dataframe)
def test_datanode_ffill(self):
data = pd.Series(range(len(self.daterange)), self.daterange, dtype=float)
data = data[[bool(i % 2) for i in range(len(data.index))]]
expected = data.reindex(self.daterange, method="ffill")
expected[np.isnan(expected)] = np.inf
node = datanode("test_datanode_ffill", data, ffill=True, missing_value=np.inf)
qnode = node.queuenode()
self._run(qnode)
value = self.ctx[qnode]
self.assertEquals(list(value), expected.values.tolist())
def test_datanode_ffill(self):
data = pd.Series(range(len(self.daterange)), self.daterange, dtype=float)
data = data[[bool(i % 2) for i in xrange(len(data.index))]]
expected = data.reindex(self.daterange, method="ffill")
expected[np.isnan(expected)] = np.inf
node = datanode("test_datanode_ffill", data, ffill=True, missing_value=np.inf)
qnode = node.queuenode()
self._run(qnode)
value = self.ctx[qnode]
self.assertEquals(list(value), expected.values.tolist())
_logger = logging.getLogger(__name__)
index = pa.Index([datetime(1991, 12, 31),
datetime(1992, 1, 1),
datetime(1992, 1, 2),
datetime(1992, 1, 3),
datetime(1992, 1, 6),
datetime(1992, 1, 7),
datetime(1992, 1, 8),
datetime(1992, 1, 9),
datetime(1992, 1, 10),
datetime(1992, 1, 13)])
data = pa.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], index=index, dtype=float)
datanode = mdf.datanode("data", data)
df = pa.DataFrame({"A" : [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
"B" : [np.nan, 9, 8, np.nan, 6, 5, 4, 3, 2, 1]},
index=index, dtype=float)
dfnode = mdf.datanode("df", df)
filter = pa.Series([False, True, False, True, False,
True, False, True, False, True], index=index)
filternode = mdf.datanode("filter", filter)
queue_expected = [data[data.index <= d].tolist() for d in index]
queue_filtered_expected = [data[filter][data[filter].index <= d].tolist() for d in index]
delay_expected = data.shift(1)
delay_filtered_expected = data[filter].shift(1).reindex(index, method="ffill")
