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.

原始信息汇总

数据集概述

基本信息

• 名称: Mario Maker 2 ninjis
• 语言: 多语言
• 许可证: CC-BY-NC-SA-4.0
• 多语言性: 多语言
• 大小: 1M<n<10M
• 源数据集: 原始数据
• 任务类别: 其他、目标检测、文本检索、令牌分类、文本生成
• 标签: 文本挖掘

数据集描述

• 内容: 包含300万ninji重播数据，总计约12.5GB。
• 收集时间: 2022年2月，历时约1个月。
• 数据来源: 使用自托管的Mario Maker 2 API收集。

数据结构

数据实例

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

数据字段

字段	类型	描述
data_id	int	关卡的数据ID
pid	string	玩家ID
time	int	运行时长，单位为毫秒
replay	bytes	该运行的重播文件，为二进制数据

数据分割

• 分割: 仅包含训练集。

数据集使用

• 加载方式: 推荐使用datasets库的流式API加载数据。
• 示例代码: python from datasets import load_dataset ds = load_dataset("TheGreatRambler/mm2_ninji", streaming=True, split="train") print(next(iter(ds)))

数据集创建

• 创建时间: 2022年2月
• 创建方法: 使用自托管的Mario Maker 2 API，注意避免对API造成过载。
• 更新计划: 无更新计划。

使用注意事项

• 内容审查: 数据集不含有害语言或描绘。