Hands-on 08-radarr-quality-profiles-searching-and-importing: Lab

name

Evaluate release candidates and manual-import mappings in an isolated sandbox

scope

All created or modified files remain under /opt/lab-classroom/class47/. The exercise does not contact indexers, download clients, or a live Radarr instance.

steps

step

1

instruction

Create the isolated classroom directory and its input files.

command

mkdir -p /opt/lab-classroom/class47 && cat > /opt/lab-classroom/class47/profile.json <<'JSON'
{
"name": "Class47 1080p",
"allowed_qualities": ["WEBDL-1080p", "Bluray-1080p"],
"quality_order": ["WEBDL-1080p", "Bluray-1080p"],
"cutoff": "Bluray-1080p",
"upgrades_allowed": true,
"minimum_custom_format_score": 0,
"maximum_size_gb": 18
}
JSON
cat > /opt/lab-classroom/class47/candidates.json <<'JSON'
[
{
"release": "Example.Movie.2024.1080p.WEB-DL.DDP5.1.H.264-GROUPA",
"movie_match": true,
"quality": "WEBDL-1080p",
"custom_format_score": 20,
"size_gb": 7.8
},
{
"release": "Example.Movie.2024.1080p.BluRay.DTS.x264-GROUPB",
"movie_match": true,
"quality": "Bluray-1080p",
"custom_format_score": 30,
"size_gb": 12.4
},
{
"release": "Example.Movie.2024.2160p.REMUX.HEVC-GROUPC",
"movie_match": true,
"quality": "Remux-2160p",
"custom_format_score": 50,
"size_gb": 55.0
},
{
"release": "Example.Movie.2024.720p.WEB-DL-GROUPD",
"movie_match": true,
"quality": "WEBDL-720p",
"custom_format_score": 5,
"size_gb": 3.2
},
{
"release": "Different.Movie.2024.1080p.BluRay-GROUPE",
"movie_match": false,
"quality": "Bluray-1080p",
"custom_format_score": 40,
"size_gb": 10.2
}
]
JSON
cat > /opt/lab-classroom/class47/imports.json <<'JSON'
[
{
"path": "/opt/lab-classroom/class47/downloads/Example.Movie.2024/Example.Movie.2024.1080p.BluRay.mkv",
"title_match": true,
"year_match": true,
"is_main_feature": true,
"quality_known": true
},
{
"path": "/opt/lab-classroom/class47/downloads/Example.Movie/feature.mkv",
"title_match": true,
"year_match": false,
"is_main_feature": true,
"quality_known": false
},
{
"path": "/opt/lab-classroom/class47/downloads/Example.Movie.2024/sample.mkv",
"title_match": true,
"year_match": true,
"is_main_feature": false,
"quality_known": true
}
]
JSON

step

2

instruction

Create a simplified teaching model. This model demonstrates policy boundaries but is not intended to reproduce every internal Radarr sorting rule.

command

cat > /opt/lab-classroom/class47/evaluate.py <<'PY'
import json
from pathlib import Path

ROOT = Path("/opt/lab-classroom/class47")
profile = json.loads((ROOT / "profile.json").read_text())
candidates = json.loads((ROOT / "candidates.json").read_text())
imports = json.loads((ROOT / "imports.json").read_text())

quality_rank = {name: rank for rank, name in enumerate(profile["quality_order"], start=1)}
accepted = []

print("RELEASE EVALUATION")
for candidate in candidates:
reasons = []
if not candidate["movie_match"]:
reasons.append("movie identity does not match")
if candidate["quality"] not in profile["allowed_qualities"]:
reasons.append("quality is not allowed by the profile")
if candidate["custom_format_score"] < profile["minimum_custom_format_score"]:
reasons.append("custom-format score is below the profile minimum")
if candidate["size_gb"] > profile["maximum_size_gb"]:
reasons.append("reported size exceeds the classroom limit")

if reasons:
    print(f"REJECT: {candidate['release']}")
    for reason in reasons:
        print(f"  - {reason}")
else:
    accepted.append(candidate)
    print(f"ACCEPTABLE: {candidate['release']}")

if accepted:
selected = max(
accepted,
key=lambda item: (quality_rank[item["quality"]], item["custom_format_score"])
)
print(f"SELECTED BY CLASSROOM POLICY: {selected['release']}")
existing_quality = "WEBDL-1080p"
cutoff_rank = quality_rank[profile["cutoff"]]
existing_rank = quality_rank[existing_quality]
selected_rank = quality_rank[selected["quality"]]
upgrade = profile["upgrades_allowed"] and selected_rank > existing_rank and selected_rank <= cutoff_rank
print(f"UPGRADE FROM {existing_quality}: {'YES' if upgrade else 'NO'}")

print("\nMANUAL IMPORT REVIEW")
for item in imports:
reasons = []
if not item["title_match"]:
reasons.append("title is not matched")
if not item["year_match"]:
reasons.append("year is ambiguous or mismatched")
if not item["is_main_feature"]:
reasons.append("file appears to be an extra or sample")
if not item["quality_known"]:
reasons.append("quality needs operator confirmation")

if reasons:
    print(f"REVIEW: {item['path']}")
    for reason in reasons:
        print(f"  - {reason}")
else:
    print(f"READY: {item['path']}")

PY

step

3

instruction

Run the evaluator and inspect every acceptance, rejection, and manual-import decision.

command

PYTHONDONTWRITEBYTECODE=1 python3 /opt/lab-classroom/class47/evaluate.py

step

4

instruction

Inspect the profile independently and identify the allowed qualities, cutoff, minimum custom-format score, and size ceiling.

command

python3 -m json.tool /opt/lab-classroom/class47/profile.json

step

5

instruction

Perform a controlled policy experiment by changing minimum_custom_format_score from 0 to 25 in the sandbox profile, rerunning the evaluator, and observing that the WEB-DL candidate is no longer acceptable while the Bluray candidate remains acceptable.

command

python3 - <<'PY'
import json
from pathlib import Path
path = Path("/opt/lab-classroom/class47/profile.json")
data = json.loads(path.read_text())
data["minimum_custom_format_score"] = 25
path.write_text(json.dumps(data, indent=2) + "\n")
PY
PYTHONDONTWRITEBYTECODE=1 python3 /opt/lab-classroom/class47/evaluate.py

step

6

instruction

Restore the original classroom policy by setting minimum_custom_format_score back to 0 and verify the restored value.

command

python3 - <<'PY'
import json
from pathlib import Path
path = Path("/opt/lab-classroom/class47/profile.json")
data = json.loads(path.read_text())
data["minimum_custom_format_score"] = 0
path.write_text(json.dumps(data, indent=2) + "\n")
print(data["minimum_custom_format_score"])
PY