# Welcome to Graph Dataset Repo
## Installation
Use following commands to install
```bash title="Installation Steps"
pip install gridai
```
## Available commands
Use following command to see available commands.
```bash
gridai --help
```
You will see something like this.
```bash
Usage: gridai [OPTIONS] COMMAND [ARGS]...
Entry point
Options:
--help Show this message and exit.
Commands:
generate-dataset Command line function to generate geojsons from...
generate-stats Function to dump stats around the dataset.
```
## How to create a dataset ?
The command `generate-dataset` can convert all opendss models available in the parent folder by recursively searching for all valid opendss models.
```bash
gridai generate-dataset -j <system-json-path>
```
This will create a sqlite db file stroing all training data in `pytorch.data.Data` format.
## How to use the dataset ?
```python
>>> from torch_geometric.data import SQLiteDatabase
>>> db = SQLiteDatabase(path="dataset.sqlite",name="data_table")
>>> len(db)
51
>>> db[0]
Data(x=[3], edge_index=[2, 2], edge_attr=[2])
```
## Getting NodeObject and EdgeObject
You can use following snippet to convert node attributes back to an instance of
`DistNodeAttrs` and edge attributes back to an `DistEdgeAttrs`.
```python
>>> from torch_geometric.data import SQLiteDatabase
>>> from gridai.interfaces import DistNodeAttrs, DistEdgeAttrs
>>> from rich import print
>>> db = SQLiteDatabase(path="dataset.sqlite",name="data_table")
>>> print(DistNodeAttrs.from_array(db[0].x[0]))
DistNodeAttrs(
node_type=<NodeType.LOAD: 2>,
active_demand_kw=5.726587772369385,
reactive_demand_kw=1.691259503364563,
active_generation_kw=0.0,
reactive_generation_kw=0.0,
phase_type=<PhaseType.NS1S2: 11>,
kv_level=0.1200888529419899
)
>>> print(DistEdgeAttrs.from_array(db[0].edge_attr[0]))
DistEdgeAttrs(
capacity_kva=25.0,
edge_type=<DistEdgeType.TRANSFORMER: 1>,
length_miles=0.0
)
```
## Plotting the dataset
You can use following command to plot the dataset.
```python
>>> from gridai.plot_dataset import plot_dataset
>>> from torch_geometric.data import SQLiteDatabase
>>> db = SQLiteDatabase(path="dataset.sqlite",name="data_table")
>>> plot_dataset(db[0])
```
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"description": "\n# Welcome to Graph Dataset Repo\n\n## Installation\n\nUse following commands to install\n```bash title=\"Installation Steps\"\npip install gridai\n```\n\n## Available commands\n\nUse following command to see available commands.\n\n```bash\ngridai --help\n```\n\nYou will see something like this.\n```bash\nUsage: gridai [OPTIONS] COMMAND [ARGS]...\n\n Entry point\n\nOptions:\n --help Show this message and exit.\n\nCommands:\n generate-dataset Command line function to generate geojsons from...\n generate-stats Function to dump stats around the dataset.\n```\n\n## How to create a dataset ?\n\nThe command `generate-dataset` can convert all opendss models available in the parent folder by recursively searching for all valid opendss models.\n\n```bash\ngridai generate-dataset -j <system-json-path>\n```\n\nThis will create a sqlite db file stroing all training data in `pytorch.data.Data` format.\n\n## How to use the dataset ?\n\n```python\n>>> from torch_geometric.data import SQLiteDatabase\n>>> db = SQLiteDatabase(path=\"dataset.sqlite\",name=\"data_table\")\n>>> len(db)\n51\n>>> db[0]\nData(x=[3], edge_index=[2, 2], edge_attr=[2])\n```\n\n## Getting NodeObject and EdgeObject\n\nYou can use following snippet to convert node attributes back to an instance of \n`DistNodeAttrs` and edge attributes back to an `DistEdgeAttrs`.\n\n```python\n>>> from torch_geometric.data import SQLiteDatabase\n>>> from gridai.interfaces import DistNodeAttrs, DistEdgeAttrs\n>>> from rich import print\n>>> db = SQLiteDatabase(path=\"dataset.sqlite\",name=\"data_table\")\n>>> print(DistNodeAttrs.from_array(db[0].x[0]))\nDistNodeAttrs(\n node_type=<NodeType.LOAD: 2>,\n active_demand_kw=5.726587772369385,\n reactive_demand_kw=1.691259503364563,\n active_generation_kw=0.0,\n reactive_generation_kw=0.0,\n phase_type=<PhaseType.NS1S2: 11>,\n kv_level=0.1200888529419899\n)\n>>> print(DistEdgeAttrs.from_array(db[0].edge_attr[0]))\nDistEdgeAttrs(\n capacity_kva=25.0,\n edge_type=<DistEdgeType.TRANSFORMER: 1>,\n length_miles=0.0\n)\n```\n\n## Plotting the dataset\n\nYou can use following command to plot the dataset.\n\n```python\n>>> from gridai.plot_dataset import plot_dataset\n>>> from torch_geometric.data import SQLiteDatabase\n>>> db = SQLiteDatabase(path=\"dataset.sqlite\",name=\"data_table\")\n>>> plot_dataset(db[0])\n```",
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