push sheeet

pkgs/by-name/fl/flattenReferencesGraph/src/flatten_references_graph/pipe.py

@@ -0,0 +1,80 @@
+from toolz import curried as tlz
+from toolz import curry
+
+from . import lib as lib
+from . import subcomponent as subcomponent
+from .popularity_contest import popularity_contest
+from .split_paths import split_paths
+
+from .lib import (
+    # references_graph_to_igraph
+    debug,
+    pick_attrs
+)
+
+funcs = tlz.merge(
+    pick_attrs(
+        [
+            "flatten",
+            "over",
+            "split_every",
+            "limit_layers",
+            "remove_paths",
+            "reverse"
+        ],
+        lib
+    ),
+    pick_attrs(
+        [
+            "subcomponent_in",
+            "subcomponent_out",
+        ],
+        subcomponent
+    ),
+    {
+        "split_paths": split_paths,
+        "popularity_contest": popularity_contest,
+        "map": tlz.map
+    }
+)
+
+
+@curry
+def nth_or_none(index, xs):
+    try:
+        return xs[index]
+    except IndexError:
+        return None
+
+
+def preapply_func(func_call_data):
+    [func_name, *args] = func_call_data
+    debug("func_name", func_name)
+    debug("args", args)
+    debug('func_name in ["over"]', func_name in ["over"])
+
+    # TODO: these could be handled in more generic way by defining, for each
+    # function, which of the args are expected to be functions which need
+    # pre-applying.
+    if func_name == "over":
+        [first_arg, second_arg] = args
+        args = [first_arg, preapply_func(second_arg)]
+
+    elif func_name == "map":
+        args = [preapply_func(args[0])]
+
+    return funcs[func_name](*args)
+
+
+@curry
+def pipe(pipeline, data):
+    debug("pipeline", pipeline)
+    partial_funcs = list(tlz.map(preapply_func, pipeline))
+    debug('partial_funcs', partial_funcs)
+    return tlz.pipe(
+        data,
+        *partial_funcs
+    )
+
+
+funcs["pipe"] = pipe