mirror of
https://github.com/wassname/catalyst.git
synced 2026-08-18 11:50:11 +08:00
64 lines
1.8 KiB
Python
64 lines
1.8 KiB
Python
from itertools import chain
|
|
|
|
from zipline.gens.utils import roundrobin, done_message
|
|
from zipline.gens.sort import date_sort
|
|
|
|
def date_sorted_sources(*sources):
|
|
"""
|
|
Takes an iterable of sources, generating namestrings and
|
|
piping their output into date_sort.
|
|
"""
|
|
|
|
for source in sources:
|
|
assert iter(source), "Source %s not iterable" % source
|
|
assert hasattr(source, 'get_hash'), "No get_hash"
|
|
|
|
# Get name hashes to pass to date_sort.
|
|
names = [source.get_hash() for source in sources]
|
|
|
|
# Convert the list of generators into a flat stream by pulling
|
|
# one element at a time from each.
|
|
stream_in = roundrobin(sources, names)
|
|
|
|
# Guarantee the flat stream will be sorted by date, using
|
|
# source_id as tie-breaker, which is fully deterministic (given
|
|
# deterministic string representation for all args/kwargs)
|
|
|
|
return date_sort(stream_in, names)
|
|
|
|
def sequential_transforms(stream_in, *transforms):
|
|
"""
|
|
Apply each transform in transforms sequentially to each event in stream_in.
|
|
Each transform application will add a new entry indexed to the transform's
|
|
hash string.
|
|
"""
|
|
|
|
assert isinstance(transforms, (list, tuple))
|
|
|
|
for tnfm in transforms:
|
|
tnfm.sequential = True
|
|
tnfm.merged = False
|
|
|
|
# Recursively apply all transforms to the stream.
|
|
stream_out = reduce(lambda stream, tnfm: tnfm.transform(stream),
|
|
transforms,
|
|
stream_in)
|
|
|
|
|
|
dt_aliased = alias_dt(stream_out)
|
|
return add_done(dt_aliased)
|
|
|
|
|
|
def alias_dt(stream_in):
|
|
"""
|
|
Alias the dt field to datetime on each message.
|
|
"""
|
|
for message in stream_in:
|
|
message['datetime'] = message['dt']
|
|
yield message
|
|
|
|
|
|
# Add a done message to a stream.
|
|
def add_done(stream_in):
|
|
return chain(stream_in, [done_message('Composite')])
|