Add GObject Introspection integration

- Parse .gir files
- Validate class, property, and signal names

gtkblueprinttool/utils.py

@@ -0,0 +1,67 @@
+# utils.py
+#
+# Copyright 2021 James Westman <james@jwestman.net>
+#
+# This file is free software; you can redistribute it and/or modify it
+# under the terms of the GNU Lesser General Public License as
+# published by the Free Software Foundation; either version 3 of the
+# License, or (at your option) any later version.
+#
+# This file is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this program.  If not, see <http://www.gnu.org/licenses/>.
+#
+# SPDX-License-Identifier: LGPL-3.0-or-later
+
+import typing as T
+
+
+def lazy_prop(func):
+    key = "_lazy_prop_" + func.__name__
+
+    @property
+    def real_func(self):
+        if key not in self.__dict__:
+            self.__dict__[key] = func(self)
+        return self.__dict__[key]
+
+    return real_func
+
+
+def did_you_mean(word: str, options: [str]) -> T.Optional[str]:
+    if len(options) == 0:
+        return None
+
+    def levenshtein(a, b):
+        # see https://en.wikipedia.org/wiki/Levenshtein_distance
+        m = len(a)
+        n = len(b)
+
+        distances = [[0 for j in range(n)] for i in range(m)]
+
+        for i in range(m):
+            distances[i][0] = i
+        for j in range(n):
+            distances[0][j] = j
+
+        for j in range(1, n):
+            for i in range(1, m):
+                cost = 0
+                if a[i] != b[j]:
+                    if a[i].casefold() == b[j].casefold():
+                        cost = 1
+                    else:
+                        cost = 2
+                distances[i][j] = min(distances[i-1][j] + 2, distances[i][j-1] + 2, distances[i-1][j-1] + cost)
+
+        return distances[m-1][n-1]
+
+    distances = [(option, levenshtein(word, option)) for option in options]
+    closest = min(distances, key=lambda item:item[1])
+    if closest[1] <= 5:
+        return closest[0]
+    return None