Datasets:

TheGreatRambler
/

mm2_ninji

	---
	language:
	- multilingual
	license:
	- cc-by-nc-sa-4.0
	multilinguality:
	- multilingual
	size_categories:
	- 1M<n<10M
	source_datasets:
	- original
	task_categories:
	- other
	- object-detection
	- text-retrieval
	- token-classification
	- text-generation
	task_ids: []
	pretty_name: Mario Maker 2 ninjis
	tags:
	- text-mining
	---

	# Mario Maker 2 ninjis
	Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)

	## Dataset Description
	The Mario Maker 2 ninjis dataset consists of 3 million ninji replays from Nintendo's online service totaling around 12.5GB of data. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022.

	### How to use it
	The Mario Maker 2 ninjis dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following code:

	```python
	from datasets import load_dataset

	ds = load_dataset("TheGreatRambler/mm2_ninji", streaming=True, split="train")
	print(next(iter(ds)))

	#OUTPUT:
	{
	'data_id': 12171034,
	'pid': '4748613890518923485',
	'time': 83388,
	'replay': [some binary data]
	}
	```
	Each row is a ninji run in the level denoted by the `data_id` done by the player denoted by the `pid`, The length of this ninji run is `time` in milliseconds.

	`replay` is a gzip compressed binary file format describing the animation frames and coordinates of the player throughout the run. Parsing the replay is as follows:

	```python
	from datasets import load_dataset
	import zlib
	import struct

	ds = load_dataset("TheGreatRambler/mm2_ninji", streaming=True, split="train")
	row = next(iter(ds))
	replay = zlib.decompress(row["replay"])

	frames = struct.unpack(">I", replay[0x10:0x14])[0]
	character = replay[0x14]

	character_mapping = {
	0: "Mario",
	1: "Luigi",
	2: "Toad",
	3: "Toadette"
	}

	# player_state is between 0 and 14 and varies between gamestyles
	# as outlined below. Determining the gamestyle of a particular run
	# and rendering the level being played requires TheGreatRambler/mm2_ninji_level
	player_state_base = {
	0: "Run/Walk",
	1: "Jump",
	2: "Swim",
	3: "Climbing",
	5: "Sliding",
	7: "Dry bones shell",
	8: "Clown car",
	9: "Cloud",
	10: "Boot",
	11: "Walking cat"
	}

	player_state_nsmbu = {
	4: "Sliding",
	6: "Turnaround",
	10: "Yoshi",
	12: "Acorn suit",
	13: "Propeller active",
	14: "Propeller neutral"
	}

	player_state_sm3dw = {
	4: "Sliding",
	6: "Turnaround",
	7: "Clear pipe",
	8: "Cat down attack",
	13: "Propeller active",
	14: "Propeller neutral"
	}

	player_state_smb1 = {
	4: "Link down slash",
	5: "Crouching"
	}

	player_state_smw = {
	10: "Yoshi",
	12: "Cape"
	}

	print("Frames: %d\nCharacter: %s" % (frames, character_mapping[character]))

	current_offset = 0x3C
	# Ninji updates are reported every 4 frames
	for i in range((frames + 2) // 4):
	flags = replay[current_offset] >> 4
	player_state = replay[current_offset] & 0x0F
	current_offset += 1

	x = struct.unpack("<H", replay[current_offset:current_offset + 2])[0]
	current_offset += 2
	y = struct.unpack("<H", replay[current_offset:current_offset + 2])[0]
	current_offset += 2

	if flags & 0b00000110:
	unk1 = replay[current_offset]
	current_offset += 1

	in_subworld = flags & 0b00001000

	print("Frame %d:\n Flags: %s,\n Animation state: %d,\n X: %d,\n Y: %d,\n In subworld: %s"
	% (i, bin(flags), player_state, x, y, in_subworld))

	#OUTPUT:
	Frames: 5006
	Character: Mario
	Frame 0:
	Flags: 0b0,
	Animation state: 0,
	X: 2672,
	Y: 2288,
	In subworld: 0
	Frame 1:
	Flags: 0b0,
	Animation state: 0,
	X: 2682,
	Y: 2288,
	In subworld: 0
	Frame 2:
	Flags: 0b0,
	Animation state: 0,
	X: 2716,
	Y: 2288,
	In subworld: 0
	...
	Frame 1249:
	Flags: 0b0,
	Animation state: 1,
	X: 59095,
	Y: 3749,
	In subworld: 0
	Frame 1250:
	Flags: 0b0,
	Animation state: 1,
	X: 59246,
	Y: 3797,
	In subworld: 0
	Frame 1251:
	Flags: 0b0,
	Animation state: 1,
	X: 59402,
	Y: 3769,
	In subworld: 0
	```

	You can also download the full dataset. Note that this will download ~12.5GB:
	```python
	ds = load_dataset("TheGreatRambler/mm2_ninji", split="train")
	```

	## Data Structure

	### Data Instances

	```python
	{
	'data_id': 12171034,
	'pid': '4748613890518923485',
	'time': 83388,
	'replay': [some binary data]
	}
	```

	### Data Fields

	\|Field\|Type\|Description\|
	\|---\|---\|---\|
	\|data_id\|int\|The data ID of the level this run occured in\|
	\|pid\|string\|Player ID of the player\|
	\|time\|int\|Length in milliseconds of the run\|
	\|replay\|bytes\|Replay file of this run\|

	### Data Splits

	The dataset only contains a train split.

	<!-- TODO create detailed statistics -->

	## Dataset Creation

	The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.

	## Considerations for Using the Data

	The dataset contains no harmful language or depictions.