semvec/pypi-packages huggingface dataset:
gridwiz-analytics 0.1.0
Data analysis utilities for GridWiz (extracted from notebook)
License: MIT
Requires Python: >=3.8
Keywords: analysis, analytics, data, gridwiz

# gridwiz_analytics

Utilities to analyze GridWiz e-bike data from Excel files. **You provide the file at call-time** (e.g., `July.xlsx`).

## Install
```bash
pip install .
# or build first:
python -m build && pip install dist/*.whl
```

## Usage
```python
from gridwiz_analytics import (
    RevenueTrendAnalyzer, PeakUsageAnalyzer,
    HourlyPeakAnalyzer, BikeUsageAnalyzer,
    DailyRevenueAnalyzer, DailyRidingAnalyzer
)

# Revenue per parking spot (read Excel when you instantiate)
rev = RevenueTrendAnalyzer("July.xlsx")
print(rev.total_revenue_by_spot())
rev.plot_revenue_by_spot(selected_spots=[
    "Gridwiz Parking Area","Fakultas Teknik","Fakultas Kedokteran"
])

# Peak usage by day for selected spots
peak = PeakUsageAnalyzer("Agustus.xlsx")
peak.plot_daily_peak_usage(["Islamic Center"])

# Hourly usage for top-N spots
hourly = HourlyPeakAnalyzer("august.xlsx")
hourly.plot_hourly_usage_top_spots(from_spots=["Islamic Center","UTTARA"], top_n=5)

# Total usage counts (filtered list)
bike = BikeUsageAnalyzer("data_july.xlsx")
bike.plot_usage([
    "Gridwiz Parking Area","Fakultas Teknik","UPT Perpustakaan","Rektorat"
])

# Daily revenue per spot (wide date range filled with zeros)
daily_rev = DailyRevenueAnalyzer("16-26.xlsx")
daily_rev.plot_daily_revenue(["Gridwiz Parking Area","Fakultas Teknik"])

# Daily riding counts per spot
daily_ride = DailyRidingAnalyzer("16-26.xlsx")
daily_ride.plot_daily_riding(["Islamic Center","UTTARA"], title="Daily Riding Partnership")
```
aind-metadata-manager 0.0.6
Generated from aind-library-template
License: MIT
Requires Python: >=3.10


# AIND Metadata Manager

AIND Metadata Manager is a Python package for managing, upgrading, and validating metadata files used in the Allen Institute for Neural Dynamics (AIND) data pipelines. It provides tools to upgrade metadata schemas, process and validate metadata, and support reproducible data workflows.

## Features
- Upgrade metadata files to the latest schema versions
- Validate and process metadata for AIND data pipelines
- Utilities for handling data descriptions, procedures, and processing metadata
- Command-line and programmatic interfaces

## Installation

1. Clone the repository:
	```sh
	git clone https://github.com/AllenNeuralDynamics/aind-metadata-manager.git
	cd aind-metadata-manager
	```
2. Create and activate a virtual environment (recommended):
	```sh
	python -m venv venv
	venv\Scripts\activate  # On Windows
	# or
	source venv/bin/activate  # On macOS/Linux
	```
3. Install dependencies:
	```sh
	pip install -e .
	pip install aind-data-schema aind-metadata-upgrader
	```

## Usage

### As a Python package
```python
from aind_metadata_manager.metadata_manager import MetadataManager, MetadataSettings
settings = MetadataSettings(input_dir='path/to/input', output_dir='path/to/output')
manager = MetadataManager(settings)
manager.create_processing_metadata()
```

### Command Line Interface
A CLI may be available (see `src/aind_metadata_manager/metadata_manager.py` for details):
```sh
python -m aind_metadata_manager.metadata_manager --help
```

## Development & Testing
- Tests are located in the `tests/` directory.
- To run tests:
  ```sh
  venv\Scripts\python -m unittest discover -s tests -p "test_*.py" -v
  ```

## Project Structure
- `src/aind_metadata_manager/` — Main package code
- `tests/` — Unit tests and test resources
- `docs/` — Documentation

## Requirements
- Python 3.10+
- aind-data-schema
- aind-metadata-upgrader
- pydantic, pydantic-settings

## License
This project is licensed under the terms of the MIT license. See the `LICENSE` file for details.

## Citation
If you use this package, please cite as described in `CITATION.cff`.

## Contributing
See `CONTRIBUTING.md` for guidelines.

## Contact
For questions or support, please open an issue on GitHub or contact the Allen Institute for Neural Dynamics.