semvec/pypi-packages huggingface dataset:
clipmon-cli 0.1.0
Clipboard monitor with history tracking
Requires Python: >=3.8
Keywords: automation, cli, clipboard, history, monitor

# clipmon

Clipboard monitor with history tracking. Watch for clipboard changes, maintain a searchable history, and quickly access past clips.

## Installation

```bash
pip install clipmon
```

## Quick Start

```bash
# Get current clipboard
clipmon get

# Set clipboard
clipmon set "Hello, world!"

# Watch for changes (saves to history automatically)
clipmon watch

# View history
clipmon history

# Copy a history entry back to clipboard
clipmon copy 5
```

## Commands

| Command | Description |
|---------|-------------|
| `get` | Get current clipboard content |
| `set` | Set clipboard content |
| `history` | Show clipboard history |
| `show <id>` | Show full content of a history entry |
| `copy <id>` | Copy a history entry back to clipboard |
| `delete <id>` | Delete a history entry |
| `clear` | Clear all history |
| `watch` | Watch clipboard for changes |
| `save` | Save current clipboard to history |
| `stats` | Show history statistics |

## Usage Examples

### Basic Operations

```bash
# Get clipboard content
clipmon get

# Set from argument
clipmon set "Hello, world!"

# Set from file
clipmon set --file document.txt

# Set from stdin
echo "piped content" | clipmon set --stdin

# Set and save to history
clipmon set "important text" --save
```

### History Management

```bash
# Show last 20 entries
clipmon history

# Show more entries
clipmon history --limit 50

# Search history
clipmon history --search "password"

# Show full content (not truncated)
clipmon history --full

# View specific entry
clipmon show 42

# Copy entry back to clipboard
clipmon copy 42

# Delete entry
clipmon delete 42

# Clear all history
clipmon clear
```

### Watch Mode

```bash
# Watch and save to history
clipmon watch

# Watch without saving
clipmon watch --no-save

# Faster polling
clipmon watch --interval 0.5

# Quiet mode (just save, no output)
clipmon watch --quiet

# JSON output for automation
clipmon watch --json
```

### Statistics

```bash
# Show stats
clipmon stats

# JSON output
clipmon stats --json
```

## JSON Output

All commands support `--json` for machine-readable output:

```bash
clipmon get --json
# {"content": "clipboard text", "length": 14}

clipmon history --json
# [{"id": 1, "content": "...", "timestamp": 1234567890, ...}]

clipmon stats --json
# {"total_entries": 42, "total_size_bytes": 12345, ...}
```

## Storage

History is stored in a SQLite database:
- **macOS**: `~/Library/Application Support/clipmon/history.db`
- **Linux**: `~/.local/share/clipmon/history.db`
- **Windows**: `%LOCALAPPDATA%/clipmon/history.db`

## For AI Agents

See [SKILL.md](SKILL.md) for AI agent integration documentation.

## License

MIT
plothist-utils 0.0.3
Utilities for the plothist package
Requires Python: >=3.10

# plothist_utils
[![CI Status][ci-badge]][ci-link]
[![CD Status][cd-badge]][cd-link]
[![PyPI version][pypi-version]][pypi-link]
[![Conda-Forge][conda-badge]][conda-link]

Sub-package to hold the fonts and some data to make the examples of [plothist](https://github.com/8-hat/plothist).

[ci-badge]:     https://github.com/8-hat/plothist_utils/actions/workflows/ci.yaml/badge.svg
[ci-link]:      https://github.com/8-hat/plothist_utils/actions/workflows/ci.yaml?query=branch%3Amain
[cd-badge]:     https://github.com/8-hat/plothist_utils/actions/workflows/cd.yaml/badge.svg
[cd-link]:      https://github.com/8-hat/plothist_utils/actions/workflows/cd.yaml?query=branch%3Amain
[pypi-link]:    https://pypi.org/project/plothist-utils/
[pypi-version]: https://badge.fury.io/py/plothist-utils.svg
[conda-badge]:  https://img.shields.io/conda/vn/conda-forge/plothist_utils?style=platic
[conda-link]:   https://anaconda.org/conda-forge/plothist_utils

## Fonts
Fonts distributed in this package have open licenses and can be redistributed. While it would be preferable to download the files from their original repositories, there is no convenient way to do that due to varying repo structures. The following fonts are included:


#### Latin Modern Math

https://mirrors.cicku.me/ctan/fonts/lm-math/

#### Latin Modern Roman

https://www.1001fonts.com/latin-modern-roman-font.html

#### Latin Modern Sans

https://www.1001fonts.com/latin-modern-sans-font.html
labmate 0.10.6
Data management library to save data and plots to hdf5 files
Requires Python: >=3.8

# Labmate. The mate that simplifies data management in your lab.

<h1 align="center">
<img src="docs/images/labmate-preview.png" width="600">
</h1><br>

<div align="center">

[![Pypi](https://img.shields.io/pypi/v/labmate.svg)](https://pypi.org/project/labmate/)
![Python 3.7+](https://img.shields.io/badge/python-3.7%2B-blue)
[![License](https://img.shields.io/badge/license-LGPL-green)](./LICENSE)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![CodeFactor](https://www.codefactor.io/repository/github/kyrylo-gr/labmate/badge/main)](https://www.codefactor.io/repository/github/kyrylo-gr/labmate/overview/main)
[![Codecov](https://codecov.io/gh/kyrylo-gr/labmate/graph/badge.svg)](https://codecov.io/gh/kyrylo-gr/labmate)
[![Download Stats](https://img.shields.io/pypi/dm/labmate)](https://pypistats.org/packages/labmate)
[![Documentation](https://img.shields.io/badge/docs-blue)](https://kyrylo-gr.github.io/labmate/)

</div>

This library facilitates the clear division of data acquisition from analysis. It provides robust tools for efficient data management and includes features to ensure a further use of the saved data.

This library is based on the other library [dh5](https://kyrylo-gr.github.io/dh5/) which is a wrapper around the h5py library.

## Install

`pip install labmate`

## Installation in dev mode

`pip install -e .[dev]` or `python setup.py develop`

## Usage

Setup:

```python
from labmate.acquisition_notebook import AcquisitionAnalysisManager
aqm = AcquisitionAnalysisManager("path/to/database")
```

Example of an acquisition cell. The variables x and y, along with the acquisition cell code and additional parameters that can be set, will be saved inside an h5 file.

```python
aqm.acquisition_cell("your_experiment_name")
...
aqm.save_acquisition(x=x, y=y)
```

Example of an analysis cell. You cannot directly use the `x` or `y` variables, as you would not be able to open them afterwards. Therefore, whenever you use variables inside an analysis cell, retrieve them from `aqm.data`.

```python
aqm.analysis_cell()

data = aqm.data
fig, ax = plt.subplots(1, 1)
ax.plot(data.x, data.y)

aqm.save_fig(fig)
```

You can find this example [here](https://github.com/kyrylo-gr/labmate/blob/main/docs/examples/aqm_simple_example.ipynb).

## More usage

To see more look at [the documentation](https://kyrylo-gr.github.io/labmate/)
ibl-neuropixel 1.10.0
Collection of tools for Neuropixel 1.0 and 2.0 probes data
Requires Python: >=3.10

# ibl-neuropixel
Collection of tools to handle Neuropixel 1.0 and 2.0 data
(documentation coming soon...)

## Installation
Minimum Python version supported is 3.10
`uv pip install ibl-neuropixel`


## Destriping
### Getting started

#### Compress a binary file losslessly using `mtscomp`

The mtscomp util implements fast chunked compression for neurophysiology data in a single shard.
Package repository is [here](https://github.com/int-brain-lab/mtscomp).


```python
from pathlib import Path
import spikeglx
file_spikeglx = Path('/datadisk/neuropixel/file.imec0.ap.bin')
sr = spikeglx.Reader(file_spikeglx)
sr.compress_file()
# note: you can use sr.compress_file(keep_original=False) to also remove the orginal bin file
```

#### Reading raw spikeglx file and manipulating arrays

The mtscomp util implements fast chunked compression for neurophysiology data in a single shard.
Package repository is [here](https://github.com/int-brain-lab/mtscomp).

```python
from pathlib import Path
import spikeglx

import ibldsp.voltage

file_spikeglx = Path('/datadisk/Data/neuropixel/human/Pt01.imec0.ap.bin')
sr = spikeglx.Reader(file_spikeglx)

# reads in 300ms of data
raw = sr[10_300_000:10_310_000, :sr.nc - sr.nsync].T
destripe = ibldsp.voltage.destripe(raw, fs=sr.fs, neuropixel_version=1)

# display with matplotlib backend
import ibldsp.plots
ibldsp.plots.voltageshow(raw, fs=sr.fs, title='raw')
ibldsp.plots.voltageshow(destripe, fs=sr.fs, title='destripe')

# display with QT backend
from viewephys.gui import viewephys
eqc = {}
eqc['raw'] = viewephys(raw, fs=sr.fs, title='raw')
eqc['destripe'] = viewephys(destripe, fs=sr.fs, title='destripe')
```

#### Destripe a binary file
This relies on a fast fourier transform external library: `pip install pyfftw`.

Minimal working example to destripe a neuropixel binary file. 
```python
from pathlib import Path
from ibldsp.voltage import decompress_destripe_cbin
sr_file = Path('/datadisk/Data/spike_sorting/pykilosort_tests/imec_385_100s.ap.bin')
out_file = Path('/datadisk/scratch/imec_385_100s.ap.bin')

decompress_destripe_cbin(sr_file=sr_file, output_file=out_file, nprocesses=8)
```

### Viewer

The best way to look at the results is to use [viewephys](https://github.com/oliche/viewephys),
open an ephys viewer on the raw data.

- tick the destripe box.
- move to a desired location in the file
- ctr+P will make the gain and axis the same on both windows

![alt text](./docs/raw_bin_viewer_destripe.png "Ephys viewer")

You can then move within the raw data file.

### White Paper
The following describes the methods implemented in this repository.
https://doi.org/10.6084/m9.figshare.19705522

## Contribution
Please see our [contribution guidelines](CONTRIBUTING.md) for details on how to contribute to this project.
aicodestat 0.0.1
A local-first metrics tool that analyzes how you use AI coding assistants.
Requires Python: >=3.10

## CodeStat · AI Code Metrics

> Quantify how much AI actually contributes to your codebase.

[![CI](https://img.shields.io/github/actions/workflow/status/2hangchen/CodeStat/ci.yml?style=flat-square&label=CI)](https://github.com/2hangchen/CodeStat/actions)
[![License](https://img.shields.io/github/license/2hangchen/CodeStat?style=flat-square)](LICENSE)

`CodeStat` is a local metrics tool that analyzes how you use AI coding assistants:
how many lines are generated by AI, how many are kept, and how this evolves over time.

> 中文文档见：[`README.zh-CN.md`](./README.zh-CN.md)

---

## Features

- **Global dashboard for all data**  
  - AI generated lines, adopted lines, adoption & generation rates  
  - File count, session count, quick bar chart overview

- **Multi‑dimension queries**  
  - **By file**: see how much of a file comes from AI and how much you kept  
  - **By session**: analyze one coding session with detailed diff lines  
  - **By project**: aggregate metrics for an entire repository

- **Agent / model comparison**  
  - Compare multiple sessions (agents / models / settings) side‑by‑side  
  - See which one actually produces more adopted code instead of just more tokens

- **Local‑first & privacy‑friendly**  
  - All metrics are computed locally from your own diffs  
  - No source code or prompts are sent to any remote service

- **Nice CLI UX**  
  - Rich‑based tables & colors, arrow‑key navigation  
  - Minimal but informative header (MCP status + repo info)

---

## Demo

> TODO: add real screenshots / GIFs from your terminal

- **Global dashboard**

  *(insert GIF or screenshot here)*

- **Session metrics with diff lines**

  *(insert GIF or screenshot here)*

---

## Quickstart

### Install

```bash
git clone https://github.com/2hangchen/CodeStat.git
cd CodeStat
pip install -r requirements.txt
```

> Once published to PyPI you can alternatively run:  
> `pip install codestat-ai`

### Start the CLI

```bash
python .\cli\main.py
```

Use `↑/↓` to move, `Enter` to confirm.  
Choose **“📈 Global Dashboard (All Data)”** to see an overview of your local metrics.

---

## Typical Workflows

- **Measure your own AI usage**  
  - Record one or more coding sessions with your IDE + MCP server  
  - Run `CodeStat` and inspect:
    - AI generated vs adopted lines
    - Which files receive the most AI help

- **Compare agents / models / prompts**  
  - Map different sessions to different agents / models  
  - Use **Compare Agents** to get a per‑session comparison table

- **Project‑level health check**  
  - For a given repo, run project metrics to see:
    - Where AI contributes the most
    - Whether AI‑generated code is actually being kept
population-trend 5.10.2
A population growth model package
Requires Python: >=3.9

<a href="https://www.islas.org.mx/"><img src="https://www.islas.org.mx/img/logo.svg" align="right" width="256" /></a>
# Population trend
[![codecov](https://codecov.io/gh/IslasGECI/population_trend/graph/badge.svg?token=wyxnwZypMA)](https://codecov.io/gh/IslasGECI/population_trend)
![example branch
parameter](https://github.com/IslasGECI/population_trend/actions/workflows/actions.yml/badge.svg)
![licencia](https://img.shields.io/github/license/IslasGECI/population_trend)
![languages](https://img.shields.io/github/languages/top/IslasGECI/population_trend)
![commits](https://img.shields.io/github/commit-activity/y/IslasGECI/population_trend)
![GitHub contributors](https://img.shields.io/github/contributors/IslasGECI/population_trend)

Este repo lo usamos en lambdas y cormorant.

Tiene la clase para graficas `Plotter_Population_Trend_Model`.