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diff --git a/resources/libraries/python/model/parse.py b/resources/libraries/python/model/parse.py
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+# Copyright (c) 2024 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Library for parsing results from JSON back to python objects.
+
+This is useful for vpp-csit jobs like per-patch performance verify.
+Such jobs invoke robot multiple times, each time on a different build.
+Each robot invocation may execute several test cases.
+How exactly are the results compared depends on the job type,
+but extracting just the main results from jsons (file trees) is a common task,
+so it is placed into this library.
+
+As such, the code in this file does not directly interact
+with the code in other files in this directory
+(result comparison is done outside robot invocation),
+but all files share common assumptions about json structure.
+
+The function here expects a particular tree created on a filesystem by
+a bootstrap script, including test results
+exported as json files according to a current model schema.
+This script extracts the results (according to result type)
+and joins them mapping from test IDs to lists of floats.
+Also, the result is cached into a results.json file,
+so each tree is parsed only once.
+
+The cached result does not depend on tree placement,
+so the bootstrap script may move and copy trees around
+before or after parsing.
+"""
+
+import json
+import os
+import pathlib
+
+from typing import Dict, List
+
+
+def parse(dirpath: str, fake_value: float = 1.0) -> Dict[str, List[float]]:
+    """Look for test jsons, extract scalar results.
+
+    Files other than .json are skipped, jsons without test_id are skipped.
+    If the test failed, four fake values are used as a fake result.
+
+    Units are ignored, as both parent and current are tested
+    with the same CSIT code so the unit should be identical.
+
+    The test results are sorted by test_id,
+    as the filesystem order is not deterministic enough.
+
+    The result is also cached as results.json file.
+
+    :param dirpath: Path to the directory tree to examine.
+    :param fail_value: Fake value to use for test cases that failed.
+    :type dirpath: str
+    :type fail_falue: float
+    :returns: Mapping from test IDs to list of measured values.
+    :rtype: Dict[str, List[float]]
+    :raises RuntimeError: On duplicate test ID or unknown test type.
+    """
+    if not pathlib.Path(dirpath).is_dir():
+        # This happens when per-patch runs out of iterations.
+        return {}
+    resultpath = pathlib.Path(f"{dirpath}/results.json")
+    if resultpath.is_file():
+        with open(resultpath, "rt", encoding="utf8") as file_in:
+            return json.load(file_in)
+    results = {}
+    for root, _, files in os.walk(dirpath):
+        for filename in files:
+            if not filename.endswith(".json"):
+                continue
+            filepath = os.path.join(root, filename)
+            with open(filepath, "rt", encoding="utf8") as file_in:
+                data = json.load(file_in)
+            if "test_id" not in data:
+                continue
+            name = data["test_id"]
+            if name in results:
+                raise RuntimeError(f"Duplicate: {name}")
+            if not data["passed"]:
+                results[name] = [fake_value] * 4
+                continue
+            result_object = data["result"]
+            result_type = result_object["type"]
+            if result_type == "mrr":
+                results[name] = result_object["receive_rate"]["rate"]["values"]
+            elif result_type == "ndrpdr":
+                results[name] = [result_object["pdr"]["lower"]["rate"]["value"]]
+            elif result_type == "soak":
+                results[name] = [
+                    result_object["critical_rate"]["lower"]["rate"]["value"]
+                ]
+            elif result_type == "reconf":
+                results[name] = [result_object["loss"]["time"]["value"]]
+            elif result_type == "hoststack":
+                results[name] = [result_object["bandwidth"]["value"]]
+            else:
+                raise RuntimeError(f"Unknown result type: {result_type}")
+    results = {test_id: results[test_id] for test_id in sorted(results)}
+    with open(resultpath, "wt", encoding="utf8") as file_out:
+        json.dump(results, file_out, indent=1, separators=(", ", ": "))
+    return results