def ffill_digest_frame_from_prior_values(freq,
field,
digest_frame,
pv_frame,
raw=False):
"""
Forward-fill a digest frame, falling back to the last known prior values if
necessary.
"""
# convert to ndarray if necessary
values = digest_frame
if raw and isinstance(digest_frame, pd.DataFrame):
values = digest_frame.values
nan_sids = pd.isnull(values[0])
if np.any(nan_sids):
# If we have any leading nans in the frame, use values from pv_frame to
# seed values for those sids.
key_loc = pv_frame.index.get_loc((freq.freq_str, field))
filler = pv_frame.values[key_loc, nan_sids]
values[0, nan_sids] = filler
if raw:
filled = ffill(values)
return filled
return digest_frame.ffill()
def frame_to_series(self, field, frame, columns=None):
"""
Convert a frame with a DatetimeIndex and sid columns into a series with
a sid index, using the aggregator defined by the given field.
"""
if isinstance(frame, pd.DataFrame):
columns = frame.columns
frame = frame.values
if not len(frame):
return pd.Series(
data=(0 if field == 'volume' else np.nan),
index=columns,
).values
if field in ['price', 'close']:
# shortcircuit for full last row
vals = frame[-1]
if np.all(~np.isnan(vals)):
return vals
return ffill(frame)[-1]
elif field == 'open':
return bfill(frame)[0]
elif field == 'volume':
return np.nansum(frame, axis=0)
elif field == 'high':
return np.nanmax(frame, axis=0)
elif field == 'low':
return np.nanmin(frame, axis=0)
else:
raise ValueError("Unknown field {}".format(field))
def frame_to_series(self, field, frame, columns=None):
"""
Convert a frame with a DatetimeIndex and sid columns into a series with
a sid index, using the aggregator defined by the given field.
"""
if isinstance(frame, pd.DataFrame):
columns = frame.columns
frame = frame.values
if not len(frame):
return pd.Series(
data=(0 if field == 'volume' else np.nan),
index=columns,
).values
if field in ['price', 'close_price']:
# shortcircuit for full last row
vals = frame[-1]
if np.all(~np.isnan(vals)):
return vals
return ffill(frame)[-1]
elif field == 'open_price':
return bfill(frame)[0]
elif field == 'volume':
return np.nansum(frame, axis=0)
elif field == 'high':
return np.nanmax(frame, axis=0)
elif field == 'low':
return np.nanmin(frame, axis=0)
else:
raise ValueError("Unknown field {}".format(field))
def ffill_digest_frame_from_prior_values(freq,
field,
digest_frame,
pv_frame,
raw=False):
"""
Forward-fill a digest frame, falling back to the last known prior values if
necessary.
"""
# convert to ndarray if necessary
values = digest_frame
if raw and isinstance(digest_frame, pd.DataFrame):
values = digest_frame.values
nan_sids = pd.isnull(values[0])
if np.any(nan_sids):
# If we have any leading nans in the frame, use values from pv_frame to
# seed values for those sids.
key_loc = pv_frame.index.get_loc((freq.freq_str, field))
filler = pv_frame.values[key_loc, nan_sids]
values[0, nan_sids] = filler
if raw:
filled = ffill(values)
return filled
return digest_frame.ffill()
def test_ffill():
# test ndim=1
N = 100
s = pd.Series(np.random.randn(N))
mask = random.sample(range(N), 10)
s.iloc[mask] = np.nan
correct = s.ffill().values
test = ffill(s.values)
assert_almost_equal(correct, test)
# test ndim=2
df = pd.DataFrame(np.random.randn(N, N))
df.iloc[mask] = np.nan
correct = df.ffill().values
test = ffill(df.values)
assert_almost_equal(correct, test)
def ffill_buffer_from_prior_values(freq,
field,
buffer_frame,
digest_frame,
pv_frame,
raw=False):
"""
Forward-fill a buffer frame, falling back to the end-of-period values of a
digest frame if the buffer frame has leading NaNs.
"""
# convert to ndarray if necessary
digest_values = digest_frame
if raw and isinstance(digest_frame, pd.DataFrame):
digest_values = digest_frame.values
buffer_values = buffer_frame
if raw and isinstance(buffer_frame, pd.DataFrame):
buffer_values = buffer_frame.values
nan_sids = pd.isnull(buffer_values[0])
if np.any(nan_sids) and len(digest_values):
# If we have any leading nans in the buffer and we have a non-empty
# digest frame, use the oldest digest values as the initial buffer
# values.
buffer_values[0, nan_sids] = digest_values[-1, nan_sids]
nan_sids = pd.isnull(buffer_values[0])
if np.any(nan_sids):
# If we still have leading nans, fall back to the last known values
# from before the digest.
key_loc = pv_frame.index.get_loc((freq.freq_str, field))
filler = pv_frame.values[key_loc, nan_sids]
buffer_values[0, nan_sids] = filler
if raw:
filled = ffill(buffer_values)
return filled
return buffer_frame.ffill()
def ffill_buffer_from_prior_values(freq,
field,
buffer_frame,
digest_frame,
pv_frame,
raw=False):
"""
Forward-fill a buffer frame, falling back to the end-of-period values of a
digest frame if the buffer frame has leading NaNs.
"""
# convert to ndarray if necessary
digest_values = digest_frame
if raw and isinstance(digest_frame, pd.DataFrame):
digest_values = digest_frame.values
buffer_values = buffer_frame
if raw and isinstance(buffer_frame, pd.DataFrame):
buffer_values = buffer_frame.values
nan_sids = pd.isnull(buffer_values[0])
if np.any(nan_sids) and len(digest_values):
# If we have any leading nans in the buffer and we have a non-empty
# digest frame, use the oldest digest values as the initial buffer
# values.
buffer_values[0, nan_sids] = digest_values[-1, nan_sids]
nan_sids = pd.isnull(buffer_values[0])
if np.any(nan_sids):
# If we still have leading nans, fall back to the last known values
# from before the digest.
key_loc = pv_frame.index.get_loc((freq.freq_str, field))
filler = pv_frame.values[key_loc, nan_sids]
buffer_values[0, nan_sids] = filler
if raw:
filled = ffill(buffer_values)
return filled
return buffer_frame.ffill()