Float to latex

.gitignore

@@ -2,4 +2,5 @@ __pycache__
 .mypy_cache
 .pytest_cache
 *.egg-info
-.coverage
+.coverage
+.DS_Store

pyproject.toml

@@ -8,6 +8,8 @@ authors = [
 ]
 requires-python = ">=3.13"
 dependencies = [
+    "beartype>=0.22.9",
+    "more-itertools>=10.8.0",
     "trycast>=1.2.0",
 ]
 

src/luxtools/__init__.py

@@ -1,7 +1,22 @@
-from .functional.compose import chain
-from .functional.overload import overload
-from .functional.partial import partial
-from .scientific.error_propagation import get_error
-from .scientific.printing import NumericResult
+from beartype.claw import (
+    beartype_this_package,
+)
+
+beartype_this_package()
 
-__all__ = ["chain", "partial", "overload", "get_error", "NumericResult"]
+from .functional import (
+    cascade_filter,
+    cascade_filter_safe,
+    chain,
+    chain_eager,
+    first,
+    first_safe,
+    fnot,
+    head,
+    identity,
+    isempty,
+    overload,
+    partial,
+)
+from .scientific.error_propagation import get_error
+from .scientific.printing import NumericResult

src/luxtools/functional/__init__.py

@@ -1,5 +1,21 @@
-from .compose import chain
-from .overload import overload
+from .basics import first, first_safe, fnot, head, identity, isempty
+from .cascade_filter import cascade_filter, cascade_filter_safe
+from .compose import chain, chain_eager
+from .currying import overload
 from .partial import partial
 
-all = ["chain", "partial", "overload"]
+# all = ["chain", "partial", "overload", "cascade_filter", "cascade_filter_safe"]
+all = [
+    "first",
+    "first_safe",
+    "fnot",
+    "head",
+    "identity",
+    "isempty",
+    "cascade_filter",
+    "cascade_filter_safe",
+    "chain",
+    "chain_eager",
+    "overload",
+    "partial",
+]

src/luxtools/functional/basics.py

@@ -0,0 +1,50 @@
+from collections.abc import Iterable
+from typing import Iterator, TypeVar
+
+from more_itertools import peekable
+
+from .currying import overload
+
+T = TypeVar("T")
+
+
+def identity(x: T) -> T:
+    return x
+
+
+def fnot(x: bool):
+    return not x
+
+
+def first(items: Iterable[T]):
+    return items.__iter__().__next__()
+
+
+def first_safe(items: Iterable[T]):
+    try:
+        return items.__iter__().__next__()
+    except StopIteration:
+        return None
+
+
+head = first
+
+
+@overload()
+def isempty(items: Iterator):
+    it = peekable(items)
+    try:
+        it.peek()
+        return False
+    except StopIteration:
+        return True
+
+
+@overload()
+def isempty(items: Iterable):
+    it = peekable(items)
+    try:
+        it.peek()
+        return False
+    except StopIteration:
+        return True

src/luxtools/functional/better_builtins.py

@@ -0,0 +1,72 @@
+from collections.abc import Callable, Iterable, Iterator
+from itertools import tee
+from typing import TypeVar
+
+from .basics import first, fnot
+from .compose import chain as fchain
+
+T = TypeVar("T")
+
+
+def recursive_tee(iterable, n=2):
+    """
+    A deep, recursive version of itertools.tee.
+
+    It splits 'iterable' into 'n' independent iterators.
+    Crucially, if it encounters an item that is itself an Iterator,
+    it recursively 'tees' that item into 'n' independent copies
+    before yielding it.
+    """
+    # 1. We must work with an iterator to use tee
+    it = iter(iterable)
+
+    # 2. Define a generator that processes items as they pass through.
+    #    This is the "Lazy Mapper" that handles the splitting logic.
+    def splitter():
+        for item in it:
+            if isinstance(item, Iterator):
+                # RECURSION: The item is an iterator, so we must split it
+                # into n copies (one for each branch we are creating)
+                yield recursive_tee(item, n)
+            else:
+                # BASE CASE: The item is simple (int, string, etc).
+                # Just return n references to the same object.
+                yield (item,) * n
+
+    # 3. Create a master stream that yields tuples of split items:
+    #    e.g. ( (item1_copyA, item1_copyB), (item2_copyA, item2_copyB), ... )
+    #    We tee this master stream so every output branch can access the tuples.
+    streams_of_tuples = tee(splitter(), n)
+
+    # 4. Unzip: Create n generators.
+    #    The i-th generator picks the i-th element from the stream of tuples.
+    return tuple((t[i] for t in stream) for i, stream in enumerate(streams_of_tuples))
+
+
+def dropwhile_safe(predicate, iterable):
+    """
+    A safe, deep-aware implementation of dropwhile.
+    """
+    # We iterate over the main sequence
+    outer_iter = iter(iterable)
+
+    for item in outer_iter:
+        # 1. RECURSIVE SPLIT:
+        #    Use recursive_tee instead of standard tee.
+        #    If 'item' is a nested iterator tree, both copies are fully independent.
+        check_copy, keep_copy = recursive_tee(item, 2)
+
+        # 2. CHECK:
+        #    Predicate can consume 'check_copy' arbitrarily (even deeply).
+        if predicate(check_copy):
+            continue  # Drop and move to next
+
+        # 3. YIELD & RESUME:
+        #    Yield the pristine 'keep_copy' and the rest of the outer stream.
+        yield keep_copy
+        yield from outer_iter
+        return
+
+
+def takefirst(predicate: Callable[[T], bool], items: Iterable[T]):
+    return first(iter(dropwhile_safe(fchain([fnot, predicate]), iter(items))))

src/luxtools/functional/cascade_filter.py

@@ -0,0 +1,91 @@
+from collections.abc import Callable, Iterable
+from typing import TypeVar
+
+from .basics import fnot, isempty
+from .better_builtins import takefirst
+from .compose import chain as fchain
+
+T = TypeVar("T")
+
+
+class NoItemsMatchedError(ValueError):
+    pass
+
+
+def cascade_filter(
+    filter_predicates: Iterable[Callable[[T], bool]],
+    items: Iterable[T],
+    cascade_predicate: Callable[[Iterable[T]], bool] = isempty,
+) -> Iterable[T]:
+    """
+    Apply a cascade of filters to an iterable of items.
+
+    Each predicate in `filter_predicates` is applied to `items`, producing a list of
+    matching items for that predicate. The first list that satisfies
+    `cascade_predicate` (default: any items matched) is returned.
+
+    Parameters
+    ----------
+    filter_predicates : Iterable[Callable[[T], bool]]
+        An iterable of predicate functions. Each function takes an item of type T
+        and returns True if the item should be kept.
+    items : Iterable[T]
+        The iterable of items to filter.
+    cascade_predicate : Callable[[Iterable[T]], bool], optional
+        A predicate applied to each filtered list. The first list that makes this
+        predicate return True is returned. Defaults to lambda x: any(True for _ in x)
+        (i.e., returns the first non-empty filtered list).
+
+    Returns
+    -------
+    Iterable[T]
+        The first filtered list that satisfies `cascade_predicate`.
+
+    Raises
+    ------
+    NoItemsMatchedError
+        If none of the filtered lists satisfy `cascade_predicate`.
+    """
+    filtered = map(lambda fc: filter(fc, items), filter_predicates)
+
+    try:
+        # get the first match
+        return takefirst(fchain([fnot, cascade_predicate]), iter(filtered))
+    except StopIteration:
+        raise NoItemsMatchedError(
+            f"No items satisfies the filters. Tried {len(list(filter_predicates))} filter(s)."
+        )
+
+
+def cascade_filter_safe(
+    filter_predicates: Iterable[Callable[[T], bool]],
+    items: Iterable[T],
+    cascade_predicate: Callable[[Iterable[T]], bool] = isempty,
+) -> Iterable[T]:
+    """
+    See `cascade_filter` for documentation
+    """
+    try:
+        return cascade_filter(filter_predicates, items, cascade_predicate)
+    except NoItemsMatchedError:
+        return []
+
+
+if __name__ == "__main__":
+    items = range(10)
+
+    filters = [
+        lambda x: x > 20,  # will fail
+        lambda x: x > 5,  # will work
+        lambda x: x > 1,  # will work
+    ]
+
+    filters_bad = [
+        lambda x: x > 20,  # will fail
+        lambda x: x > 25,
+        lambda x: x > 100,
+    ]
+
+    print(list(cascade_filter_safe(filters, items)))
+
+    # print(first(items))