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81 lines
1.7 KiB
Python
81 lines
1.7 KiB
Python
from toolz import curried as tlz
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from toolz import curry
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from . import lib as lib
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from . import subcomponent as subcomponent
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from .popularity_contest import popularity_contest
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from .split_paths import split_paths
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from .lib import (
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# references_graph_to_igraph
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debug,
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pick_attrs
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)
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funcs = tlz.merge(
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pick_attrs(
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[
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"flatten",
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"over",
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"split_every",
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"limit_layers",
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"remove_paths",
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"reverse"
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],
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lib
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),
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pick_attrs(
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[
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"subcomponent_in",
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"subcomponent_out",
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],
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subcomponent
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),
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{
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"split_paths": split_paths,
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"popularity_contest": popularity_contest,
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"map": tlz.map
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}
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)
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@curry
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def nth_or_none(index, xs):
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try:
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return xs[index]
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except IndexError:
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return None
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def preapply_func(func_call_data):
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[func_name, *args] = func_call_data
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debug("func_name", func_name)
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debug("args", args)
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debug('func_name in ["over"]', func_name in ["over"])
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# TODO: these could be handled in more generic way by defining, for each
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# function, which of the args are expected to be functions which need
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# pre-applying.
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if func_name == "over":
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[first_arg, second_arg] = args
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args = [first_arg, preapply_func(second_arg)]
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elif func_name == "map":
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args = [preapply_func(args[0])]
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return funcs[func_name](*args)
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@curry
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def pipe(pipeline, data):
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debug("pipeline", pipeline)
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partial_funcs = list(tlz.map(preapply_func, pipeline))
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debug('partial_funcs', partial_funcs)
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return tlz.pipe(
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data,
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*partial_funcs
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)
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funcs["pipe"] = pipe
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