cli command

ceo/cli/groups.py

@@ -14,6 +14,7 @@ from ceod.transactions.groups import (
     DeleteGroupTransaction,
 )
 
+import heapq
 
 @click.group(short_help='Perform operations on CSC groups/clubs')
 def groups():
@@ -150,10 +151,75 @@ def delete(group_name):
     resp = http_delete(f'/api/groups/{group_name}')
     handle_stream_response(resp, DeleteGroupTransaction.operations)
 
+# compute levenshtein edit distance
+def _fuzzy_match(s1, s2):
+    if len(s1) == 0:
+        return len(s2)
+    if len(s2) == 0:
+        return len(s1)
+
+    edits = []
+    for i in range(len(s2) + 1):
+        edits.append(i)
+
+    for i in range(len(s1)):
+        corner = i
+        edits[0] = i + 1
+
+        for j in range(len(s2)):
+            upper = edits[j + 1]
+            if s1[i] == s2[j]:
+                edits[j + 1] = corner
+            else:
+                m = min(corner, upper, edits[j])
+                edits[j + 1] = m + 1
+
+            corner = upper
+
+    return edits[-1]
+
+# this is needed TRUST ME
+class _fuzzy_result:
+    def __init__(self, string, score):
+        self.string = string
+        self.score = score
+
+    def __lt__(self, other):
+        return self.score < other.score
+
+    def __gt__(self, other):
+        return self.score > other.score
+
+    def __le__(self, other):
+        return self.score <= other.score
+
+    def __ge__(self, other):
+        return self.score >= other.score
+
+    def __eq__(self, other):
+        return self.score == other.score
+
+    def __ne__(self, other):
+        return self.score != other.score
+
 @groups.command(short_help='Search for groups')
 @click.argument('group_name')
-def search(group_name):
+@click.option('--count', default=10, help='number of results to show')
+def search(group_name, count):
     check_if_in_development()
     resp = http_get('/api/groups/clubs')
     result = handle_sync_response(resp)
-    print(result)
+
+    scores = []
+    for i in range(count):
+        dummy_result = _fuzzy_result(None, -99999)
+        scores.append(dummy_result)
+
+    for cn in result:
+        score = -1 * _fuzzy_match(group_name, cn)
+        result = _fuzzy_result(cn, score)
+        heapq.heappushpop(scores, result)
+
+    matches = heapq.nlargest(count, scores)
+    for result in matches:
+        if result.string: click.echo(result.string)