Example of refactoring using rope library.

tools/telemetry/third_party/rope/rope/base/oi/__init__.py

Index: tools/telemetry/third_party/rope/rope/base/oi/__init__.py
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+"""Rope object analysis and inference package
+
+Rope makes some simplifying assumptions about a python program.  It
+assumes that a program only performs assignments and function calls.
+Tracking assignments is simple and `PyName` objects handle that.  The
+main problem is function calls.  Rope uses these two approaches for
+obtaining call information:
+
+* Static object analysis: `rope.base.pycore.PyCore.analyze_module()`
+
+  It can analyze modules to obtain information about functions.  This
+  is done by analyzing function calls in a module or scope.  Currently
+  SOA analyzes the scopes that are changed while saving or when the
+  user asks to analyze a module.  That is mainly because static
+  analysis is time-consuming.
+
+* Dynamic object analysis: `rope.base.pycore.PyCore.run_module()`
+
+  When you run a module or your testsuite, when DOA is enabled, it
+  collects information about parameters passed to and objects returned
+  from functions.  The main problem with this approach is that it is
+  quite slow; Not when looking up the information but when collecting
+  them.
+
+An instance of `rope.base.oi.objectinfo.ObjectInfoManager` can be used
+for accessing these information.  It saves the data in a
+`rope.base.oi.objectdb.ObjectDB` internally.
+
+Now if our objectdb does not know anything about a function and we
+need the value returned by it, static object inference, SOI, comes
+into play.  It analyzes function body and tries to infer the object
+that is returned from it (we usually need the returned value for the
+given parameter objects).
+
+Rope might collect and store information for other `PyName`\s, too.
+For instance rope stores the object builtin containers hold.
+
+"""