refactor: remove jank fuzzy find

Author: lilatomic

alpacloud/promls/cli.py

@@ -8,7 +8,7 @@
 import click
 
 from alpacloud.promls.fetch import FetcherURL, Parser
-from alpacloud.promls.filter import MetricsTree, filter_any, filter_ish, filter_name, filter_path
+from alpacloud.promls.filter import MetricsTree, filter_any, filter_name, filter_path
 from alpacloud.promls.metrics import Metric
 from alpacloud.promls.util import paths_to_tree
 from alpacloud.promls.vis import PromlsVisApp
@@ -136,15 +136,6 @@ def path(url, filter: str, display: PrintMode):
 	do_print(filtered, display)
 
 
-@search.command()
-@common_args()
-def ish(url, filter: str, display: PrintMode):
-	"""Filter metrics using a fuzzy match"""
-	tree = do_fetch(url)
-	filtered = tree.filter(filter_ish(filter))
-	do_print(filtered, display)
-
-
 @search.command()
 @arg_url
 @opt_filter

alpacloud/promls/filter.py

@@ -61,73 +61,3 @@ def predicate(metric: Metric) -> bool:
 		return pattern.match(metric.name) is not None
 
 	return predicate
-
-
-def filter_ish(pattern: str) -> Predicate:
-	"""
-	Filter metrics for this that are kindof like what you want.
-	Uses difflib
-	"""
-
-	def predicate(metric: Metric) -> bool:
-		mismatch = letter_mismatch(metric.name, pattern)
-		if mismatch > len(pattern) / 2:
-			l.debug(f"{metric.name} letter mismatch too high: {mismatch}")
-			return False
-		distance = query_levenshtein(metric.name, pattern, 0, 0, (len(metric.name) + len(pattern)) / 4, False)
-		ratio = distance / len(pattern)
-		allowable_distance = max(0.1, 1 / len(pattern))  # 10% of the length of the pattern or 1 character
-		val = ratio - allowable_distance < EPSILON
-		l.debug(f"{metric.name} ratio: {ratio}, allowable_distance: {allowable_distance}, val: {val}")
-		return val  # epsilon comparison for floating point inexactness`
-
-	return predicate
-
-
-def letter_mismatch(s, q):
-	"""Quickly eliminate metrics that will never match."""
-
-	def letter_dict(v: str) -> dict[str, int]:
-		d: dict[str, int] = {}
-		for c in v:
-			d[c] = d.get(c, 0) + 1
-		return d
-
-	s = letter_dict(s)
-	q = letter_dict(q)
-
-	misses = 0
-	for k, v in q.items():
-		delta = v - s.get(k, 0)
-		if delta > 0:
-			misses += abs(delta)
-
-	return misses
-
-
-def query_levenshtein(s, q, si, qi, badness: float, started):
-	"""
-	Modified Levenshtein distance.
-	Tries to not impose penalties for substring matches:
-	- do not penalise advancing
-	- do not penalise differences after a complete match
-
-	args:
-	- si: start index of s
-	- qi: start index of q
-	"""
-	if badness <= 0:
-		return 1e6
-	if len(s) == si:  # unprocessed query
-		return len(q)
-	elif len(q) == qi:  # entire query is processed, so we're happy
-		return 0
-	elif s[si] == q[qi]:
-		return query_levenshtein(s, q, si + 1, qi + 1, badness, True)
-	else:
-		skip_s = query_levenshtein(s, q, si + 1, qi, badness - 1, started)
-		return min(
-			skip_s + 1 if started else skip_s,
-			1 + query_levenshtein(s, q, si, qi + 1, badness - 1, True),
-			1 + query_levenshtein(s, q, si + 1, qi + 1, badness - 1, True),
-		)

alpacloud/promls/vis.py

@@ -7,7 +7,7 @@
 from textual.widget import Widget
 from textual.widgets import Footer, Header, Input, Label, Static, Tree
 
-from alpacloud.promls.filter import MetricsTree, PredicateFactory, filter_any, filter_ish, filter_name
+from alpacloud.promls.filter import MetricsTree, PredicateFactory, filter_any, filter_name
 from alpacloud.promls.metrics import Metric
 from alpacloud.promls.util import TreeT, paths_to_tree
 
@@ -51,7 +51,6 @@ class PromlsVisApp(App):
 	BINDINGS = [
 		Binding("ctrl+f", "find", "find", priority=True),
 		Binding("ctrl+g", "goto", "goto", priority=True),
-		Binding("ctrl+z", "fuzzy_find", "fuzzy find", priority=True),
 		Binding("greater_than_sign", "expand_all", "Expand all", show=False),
 		Binding("less_than_sign", "collapse_all", "Collapse all", show=False),
 	]
@@ -85,11 +84,6 @@ async def action_find(self) -> None:
 		self.load_metrics()
 		self.focus_findbox()
 
-	async def action_fuzzy_find(self) -> None:
-		self.predicate_factory = filter_ish
-		self.load_metrics()
-		self.focus_findbox()
-
 	async def action_goto(self):
 		self.predicate_factory = lambda s: filter_name(re.compile(s))
 		self.load_metrics()