CLI Reference

gem ships a command-line interface you can invoke with python -m gem. It covers two workflows:

• parse — parse a single replay and get a summary, JSON, or Parquet output.
• batch — parse a folder (or list) of replays in parallel and write the results.

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Quick examples

# Print a match summary (default)
python -m gem my_replay.dem

# Export to JSON
python -m gem my_replay.dem --format json > match.json

# Export to Parquet files
python -m gem parse my_replay.dem --format parquet --output ./out

# Parse a whole folder in parallel
python -m gem batch replays/ --format parquet --output ./out

# Concatenate all replays into one set of DataFrames
python -m gem batch replays/ --format dataframe --output ./out

# Show a live progress bar and timing breakdown
python -m gem my_replay.dem --progress --timings

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parse — single replay

python -m gem [parse] <path> [options]

INFO

The parse keyword is optional. python -m gem match.dem is identical to python -m gem parse match.dem.

Option	Values	Default	Description
<path>	path to .dem	—	Replay file to parse
--format	summary, json, parquet	summary	Output format
--output	file or directory	stdout / cwd	Output destination. Required for parquet; optional for json (omit to print to stdout)
--progress	flag	off	Show a live phase-by-phase progress bar
--timings	flag	off	Print a timing breakdown after parsing
--quiet, -q	flag	off	Suppress banner and non-essential output
--no-banner	flag	off	Hide the ASCII art banner but keep other output

Summary output

The default format prints a Rich table with per-player KDA, gold, net worth, last hits, denies, and hero kills, grouped by team.

python -m gem my_replay.dem
python -m gem my_replay.dem --no-banner        # skip ASCII art
python -m gem my_replay.dem --quiet            # minimal output

JSON output

With --format json, the full ParsedMatch structure is serialised to JSON. Omit --output to print to stdout (useful for piping):

# Print to stdout
python -m gem my_replay.dem --format json

# Write to a file
python -m gem parse my_replay.dem --format json --output match.json

Timings go to stderr when JSON is on stdout, so piping works cleanly:

python -m gem my_replay.dem --format json --timings > match.json

Parquet output

--format parquet writes one .parquet file per DataFrame table into --output:

python -m gem parse my_replay.dem --format parquet --output ./out/
# Writes: out/players.parquet, out/combat_log.parquet, out/wards.parquet, ...

Parquet dependency

Requires pyarrow (recommended) or fastparquet:

pip install pyarrow

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batch — parallel multi-replay processing

python -m gem batch <source> [options]

<source> is either a directory path or one or more explicit .dem file paths.

Option	Values	Default	Description
<source>	directory or file list	—	Replay(s) to parse
--format	parquet, dataframe	parquet	Output format
--output	directory	—	Required. Root output directory
--workers	integer	os.cpu_count()	Number of parallel worker processes
--recursive	flag	off	Scan <source> directory recursively
--progress	flag	off	Show a Rich progress bar
--timings	flag	off	Print timing breakdown after all replays
--quiet, -q	flag	off	Suppress all non-essential output

--format parquet

Each replay gets its own subdirectory under --output:

python -m gem batch replays/ --format parquet --output ./out

# Output layout:
# out/
#   match_6789/
#     players.parquet
#     combat_log.parquet
#     wards.parquet
#     ...
#   match_6790/
#     ...

--format dataframe

All replays are concatenated into one set of DataFrames, then written as a flat set of .parquet files under --output. Each row includes a match_path column for provenance.

python -m gem batch replays/ --format dataframe --output ./out

# Output layout:
# out/
#   players.parquet       ← rows from all replays
#   combat_log.parquet
#   match.parquet
#   ...

This is equivalent to calling gem.parse_many_to_dataframe() and writing the result to disk.

Parallelism

By default gem uses all CPU cores. Cap workers for lighter load:

python -m gem batch replays/ --format parquet --output ./out --workers 4

Exit codes

The batch command exits with code 0 even when some replays fail. A summary table of failed replays is printed to stderr — always check it.

TIP

Start with --workers 4 on shared machines to leave cores free for other processes. On a dedicated parsing box, omit --workers to use all cores.

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Python API equivalents

All CLI operations have direct Python equivalents:

import gem

# parse → summary  (inspect ParsedMatch directly)
match = gem.parse("my_replay.dem")

# parse → json
json_str = gem.parse_to_json("my_replay.dem", indent=2)

# parse → parquet
gem.parse_to_parquet("my_replay.dem", output_dir="./out")

# batch → list of ParseResult
results = gem.parse_many("replays/", workers=4)

# batch → concatenated DataFrames
dfs = gem.parse_many_to_dataframe("replays/", workers=4)
dfs["players"]  # has a match_path column

# batch → parquet (one subdir per replay)
gem.parse_many_to_parquet("replays/", output_dir="./out", workers=4)

See the API Reference for full parameter documentation.