Basic info

README.md

@@ -7,5 +7,93 @@ size_categories:
 - 1K<n<10K
 ---
 
-# MMT Rocket Bodies dataset for classification
+# MMT Rocket Bodies light curve classification dataset 
+
+Dataset contains light curves of 5 rocket body types from Mini Mega Tortora database (MMT)[^1]. The dataset was created to be used as a benchmark for rocket body light curve classification.  
+
+## Dataset description
+
+## Usage
+
+```python
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("MMT_Rocket_Bodies", data_files={"train": "train.csv", "test": "test.csv"})
+>>> dataset
+DatasetDict({
+    train: Dataset({
+        features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'],
+        num_rows: 5676
+    })
+    test: Dataset({
+        features: ['label', ' id', ' part', ' period', ' mag', ' phase', ' time'],
+        num_rows: 1404
+    })
+})
+```
+
+- `label` - class name
+- `id` - unique identifier of the light curve from MMT
+- `part` - part number of the light curve 
+- `period` - rotational period of the object
+- `mag` - relative path to the magnitude values file
+- `phase` - relative path to the phase values file
+- `time` - relative path to the time values file
+
+
+### File structure
+
+- `data` directory contains 5 subdirectories, one for each class. Light curves are stored in file triplets in the following format:
+  - `<track_id>_<#part>_mag.csv` - magnitude values
+  - `<track_id>_<#part>_time.csv` - time values
+  - `<track_id>_<#part>_phase.csv` - phase angle values
+
+  where `<track_id>` is the unique identifier of the light curve from MMT, `<\#part>` is the part number of the light curve (some light curves are split into multiple parts).
+- `train.csv` and `test.csv` - contains information about the train and test splits (label, id, part, period, mag, phase, time)
+- `mean_std.csv` - contains mean and standard deviation for magnitudes, computed over the training set.
+
+```
+MMT Rocket Bodies
+├── README.md
+├── train.csv
+├── test.csv
+├── mean_std.csv
+├── data
+│   ├──  ARIANE 5 R_B
+│   │   ├── <track_id>_<\#part>_mag.csv
+│   │   ├── <track_id>_<\#part>_time.csv
+│   │   ├── <track_id>_<\#part>_phase.csv
+│   ├── ATLAS 5 CENTAUR R_B
+│   │   ├── ...
+│   ├── CZ-3B R_B
+│   │   ├── ...
+│   ├── DELTA 4 R_B
+│   │   ├── ...
+│   ├── FALCON 9 R_B
+│   │   ├── ...
+│   ├── H-2A R_B
+│   │   ├── ...
+```
+
+
+## Data preprocessing
+
+To create data sutable for both CNN and RNN based models, the light curves were preprocessed in the following way:
+
+1. Split the light curves if the gap between two consecutive measurements is larger than object's rotational period.
+2. Split the light curves to have maximum span 1_000 seconds.
+3. Filter out light curves which folded form divided into 100 bins has more than 25% of bins empty.
+5. Resample the light curves to 10_000 points with step 0.1 seconds.
+4. Filter out light curves with less than 100 measurements.
+
+
+
+
+
+
+
+
+
+
+[^1]: Karpov, S., et al. "Mini-Mega-TORTORA wide-field monitoring system with sub-second temporal resolution: first year of operation." Revista Mexicana de Astronomía y Astrofísica 48 (2016): 91-96.