Create README

README.md

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+---
+language:
+- multilingual
+license:
+- cc-by-nc-sa-4.0
+multilinguality:
+- multilingual
+size_categories:
+- 100M<n<1B
+source_datasets:
+- original
+task_categories:
+- text-generation
+- structure-prediction
+- object-detection
+- text-mining
+- information-retrieval
+- other
+task_ids:
+- other
+pretty_name: Mario Maker 2 ninjis
+---
+
+# 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 Statistics
+
+The dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:
+
+![dataset-statistics](https://huggingface.co/datasets/codeparrot/github-code/resolve/main/github-code-stats-alpha.png)
+
+|    | Language     |File Count| Size (GB)|
+|---:|:-------------|---------:|-------:|
+|  0 | Java         | 19548190 | 107.70 |
+|  1 | C            | 14143113 | 183.83 |
+|  2 | JavaScript   | 11839883 |  87.82 |
+|  3 | HTML         | 11178557 | 118.12 |
+|  4 | PHP          | 11177610 |  61.41 |
+|  5 | Markdown     |  8464626 |  23.09 |
+|  6 | C++          |  7380520 |  87.73 |
+|  7 | Python       |  7226626 |  52.03 |
+|  8 | C#           |  6811652 |  36.83 |
+|  9 | Ruby         |  4473331 |  10.95 |
+| 10 | GO           |  2265436 |  19.28 |
+| 11 | TypeScript   |  1940406 |  24.59 |
+| 12 | CSS          |  1734406 |  22.67 |
+| 13 | Shell        |  1385648 |   3.01 |
+| 14 | Scala        |   835755 |   3.87 |
+| 15 | Makefile     |   679430 |   2.92 |
+| 16 | SQL          |   656671 |   5.67 |
+| 17 | Lua          |   578554 |   2.81 |
+| 18 | Perl         |   497949 |   4.70 |
+| 19 | Dockerfile   |   366505 |   0.71 |
+| 20 | Haskell      |   340623 |   1.85 |
+| 21 | Rust         |   322431 |   2.68 |
+| 22 | TeX          |   251015 |   2.15 |
+| 23 | Batchfile    |   236945 |   0.70 |
+| 24 | CMake        |   175282 |   0.54 |
+| 25 | Visual Basic |   155652 |   1.91 |
+| 26 | FORTRAN      |   142038 |   1.62 |
+| 27 | PowerShell   |   136846 |   0.69 |
+| 28 | Assembly     |    82905 |   0.78 |
+| 29 | Julia        |    58317 |   0.29 |
+-->
+
+## 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.