semvec/pypi-packages huggingface dataset:
ezmsg-nwb 1.0.0
NWB (Neurodata Without Borders) file reading and writing for ezmsg
Requires Python: >=3.10

# ezmsg-nwb

NWB (Neurodata Without Borders) file reading and writing for the [ezmsg](https://www.ezmsg.org) framework.

## Overview

`ezmsg-nwb` provides streaming NWB file I/O as ezmsg Units.

Key features:

* **NWB Reader** - Stream data from NWB files (local or remote) as AxisArray messages
* **NWB Writer** - Write incoming AxisArray streams to NWB files with automatic container management
* **Flexible clock handling** - Support for system, monotonic, and unknown reference clocks

## Installation

Install from PyPI:

```bash
pip install ezmsg-nwb
```

Or install the latest development version:

```bash
pip install git+https://github.com/ezmsg-org/ezmsg-nwb@main
```

## Dependencies

- `ezmsg`
- `ezmsg-baseproc`
- `numpy`
- `pynwb`
- `h5py`
- `neuroconv`
- `remfile`
- `pyyaml`

## Usage

See the `examples` folder for usage examples.

```python
import ezmsg.core as ez
from ezmsg.nwb import NWBIteratorUnit, NWBSink
```

For general ezmsg tutorials and guides, visit [ezmsg.org](https://www.ezmsg.org).

## Development

We use [`uv`](https://docs.astral.sh/uv/getting-started/installation/) for development.

1. Install [`uv`](https://docs.astral.sh/uv/getting-started/installation/) if not already installed.
2. Fork this repository and clone your fork locally.
3. Open a terminal and `cd` to the cloned folder.
4. Run `uv sync` to create a `.venv` and install dependencies.
5. (Optional) Install pre-commit hooks: `uv run pre-commit install`
6. After making changes, run the test suite: `uv run pytest tests`

## License

MIT License - see [LICENSE](LICENSE) for details.

## Acknowledgements

This project is supported by [the Wyss Center for Bio and Neuroengineering](https://wysscenter.ch)
and by [Blackrock Neurotech](https://www.blackrockneurotech.com).
varshas-math-lib 0.1.0
A simple math library for basic arithmetic operations
Requires Python: >=3.7

# Varsha Math Lib

A simple Python math library for basic arithmetic operations.

## Features

- Addition
- Subtraction
- Multiplication
- Division

## Installation

pip install varsha-math-lib

## Usage

```python
from math_lib import add

print(add(5, 3))
wrtds 0.1.0
A transcription of the usgs r package wrtds to python
Requires Python: >=3.10

# wrtds-py

A Python implementation of **WRTDS** (Weighted Regressions on Time, Discharge, and Season), the USGS method for estimating long-term trends in river water quality.

[![build](https://github.com/mullenkamp/wrtds-py/workflows/Build/badge.svg)](https://github.com/mullenkamp/wrtds-py/actions)
[![codecov](https://codecov.io/gh/mullenkamp/wrtds-py/branch/master/graph/badge.svg)](https://codecov.io/gh/mullenkamp/wrtds-py)
[![PyPI version](https://badge.fury.io/py/wrtds.svg)](https://badge.fury.io/py/wrtds)

---

**Documentation**: [https://mullenkamp.github.io/wrtds-py/](https://mullenkamp.github.io/wrtds-py/)

**Source Code**: [https://github.com/mullenkamp/wrtds-py](https://github.com/mullenkamp/wrtds-py)

---

## Overview

This package is a Python transcription of the USGS R package [EGRET](https://github.com/DOI-USGS/EGRET). It uses pandas DataFrames as the base data structure with scipy for optimization and interpolation and matplotlib for plotting.

Key features:

- **Weighted censored regression** — locally weighted MLE with tricube kernels on time, discharge, and season
- **Flow normalization** — isolate water-quality trends from discharge variability
- **WRTDS-K** — AR(1) residual interpolation for improved daily estimates
- **Trend analysis** — pairwise, group, and time-series decomposition (CQTC/QTC)
- **Bootstrap confidence intervals** — block resampling with bias correction
- **Plotting** — data overview, annual histories, contour surfaces, and diagnostics

## Installation

```bash
pip install wrtds
```

Requires Python >= 3.10.

## Quick Example

```python
import pandas as pd
from wrtds import WRTDS

daily = pd.read_csv('daily.csv', parse_dates=['Date'])
sample = pd.read_csv('sample.csv', parse_dates=['Date'])

w = WRTDS(daily, sample, info={'station_name': 'Choptank River'})
w.fit()
w.kalman()

print(w.table_results())
print(w.run_pairs(year1=1985, year2=2010))
w.plot_conc_hist()
```

See the [Quickstart](https://mullenkamp.github.io/wrtds/getting-started/quickstart/) for a full walkthrough.

## Development

We use [uv](https://docs.astral.sh/uv/) to manage the development environment.

```bash
uv sync            # install dependencies
uv run pytest      # run tests
```

## License

This project is licensed under the terms of the Apache Software License 2.0.