semvec/pypi-packages huggingface dataset:
cdef-pil-090126-2205 0.1.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, &registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Config Path Aliases
You can reference `[paths]` entries directly from `registry_pattern` to avoid repeating paths.
When a pattern equals a `*_path` key, it expands to that path and automatically appends
`/*.parquet` if no glob or `.parquet` suffix is provided. You can also set `base_path`
to resolve `/path/to/...` placeholders or relative patterns in covariates.

Example:
```toml
[paths]
base_path = "E:/workdata/708245/data/registers"
bef_path = "E:/workdata/708245/data/registers/bef"
akm_path = "E:/workdata/708245/data/registers/akm"

[[covariates]]
label = "employment"
registry_pattern = "akm_path"
column = "SOCIO13"

[[covariates]]
label = "education"
registry_pattern = "/path/to/uddf/*.parquet"
column = "HFAUDD"
```

## Pipeline Cache
To speed repeat runs, cache each phase under `[output]` and enable resume:
```toml
[output]
output_dir = "output"
cache_dir = "output/cache"
resume = true
# clean_cache = true
```

CLI equivalents: `--resume`, `--clean-cache`, `--cache-dir`.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`
weni-commons 1.1.2
Weni's common utilities for Python backends
Requires Python: <4.0,>=3.8

# Weni Commons

A Python library that centralizes common functionality and utilities used across Weni's Django backend applications.
This library promotes code reuse, consistency, and maintainability by providing shared components and utilities that can be easily integrated into multiple Django projects.

## Installation

### Using Poetry (Recommended for Weni projects)

```bash
poetry add weni-commons
```

### Using pip

```bash
pip install weni-commons
```

## Available Features

### Feature Flags

Weni Feature Flags is a Python Library that functions as an abstraction layer between Django projects and GrowthBook.

```python
from weni.feature_flags.services import FeatureFlagsService

# Use the feature flags service
feature_service = FeatureFlagsService()
```

You can access the complete instructions on how to use its features [here](https://github.com/weni-ai/weni-feature-flags/blob/main/README.md).

## Requirements

- Python >= 3.8
- Django >= 3.2.22
- Django REST Framework >= 3.12.0
- Celery >= 5.0.0
- Redis (via django-redis >= 4.0.0)

## Contributing

This library is designed to grow with Weni's needs. If you have common utilities that could benefit multiple Django projects, consider contributing them to this library.

## License

This project is licensed under the GNU General Public License v3.0 - see the [LICENSE](LICENSE) file for details.
async-pcloud 0.3.0
Async python API client for PCloud
Requires Python: >=3.9
Keywords: Python, pCloud, async, REST

# async_pcloud - Async python API client for PCloud

this in an indirect fork of https://github.com/tomgross/pcloud/

[![Package test status](https://github.com/Noob-Lol/async_pcloud/actions/workflows/python-package.yml/badge.svg)](https://github.com/Noob-Lol/async_pcloud/actions)
[![PyPI version](https://img.shields.io/pypi/v/async_pcloud.svg)](https://pypi.org/project/async_pcloud)

## Difference
It's async, uses aiohttp. I plan to reimplement all functions, and add extra - implement some of the NotImplemented, make useful combinations.

update: pcloud has too many methods, i won't implement all of them

## why i made this?
because i have not found any async packages for pcloud

## Installation
```sh
# install from PyPI
pip install async_pcloud
```

## Examples
there are 2 ways to use the AsyncPyCloud.

1. manual connect, disconnect (needed for session, because this is async):
```py
import asyncio
from async_pcloud import AsyncPyCloud
pcloud = AsyncPyCloud("token", endpoint="api")

async def main():
    await pcloud.connect()
    data = await pcloud.listfolder(folderid=0)
    print(data)
    # when you're done
    await pcloud.disconnect()

asyncio.run(main())
```

2. async with - auto connect, disconnect:
```py
pcloud = AsyncPyCloud("token")

async def main():
    async with pcloud:
    # 'async with AsyncPyCloud("token") as pcloud:' will also work
        data = await pcloud.listfolder(folderid=0)
        print(data)
```

## Class arguments
- AsyncPyCloud
  - token - the api token, you can generate one with get_auth()
  - endpoint - can be 'api' or 'eapi', choose the one used by your account
  - folder - base folder name, will be added before the path param
  - headers - you can make custom user agent or something
cdef-pil-090126-2200 0.1.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, &registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Config Path Aliases
You can reference `[paths]` entries directly from `registry_pattern` to avoid repeating paths.
When a pattern equals a `*_path` key, it expands to that path and automatically appends
`/*.parquet` if no glob or `.parquet` suffix is provided. You can also set `base_path`
to resolve `/path/to/...` placeholders or relative patterns in covariates.

Example:
```toml
[paths]
base_path = "E:/workdata/708245/data/registers"
bef_path = "E:/workdata/708245/data/registers/bef"
akm_path = "E:/workdata/708245/data/registers/akm"

[[covariates]]
label = "employment"
registry_pattern = "akm_path"
column = "SOCIO13"

[[covariates]]
label = "education"
registry_pattern = "/path/to/uddf/*.parquet"
column = "HFAUDD"
```

## Pipeline Cache
To speed repeat runs, cache each phase under `[output]` and enable resume:
```toml
[output]
output_dir = "output"
cache_dir = "output/cache"
resume = true
# clean_cache = true
```

CLI equivalents: `--resume`, `--clean-cache`, `--cache-dir`.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`
cdef-pil-090126-2145 0.1.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, &registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Config Path Aliases
You can reference `[paths]` entries directly from `registry_pattern` to avoid repeating paths.
When a pattern equals a `*_path` key, it expands to that path and automatically appends
`/*.parquet` if no glob or `.parquet` suffix is provided. You can also set `base_path`
to resolve `/path/to/...` placeholders or relative patterns in covariates.

Example:
```toml
[paths]
base_path = "E:/workdata/708245/data/registers"
bef_path = "E:/workdata/708245/data/registers/bef"
akm_path = "E:/workdata/708245/data/registers/akm"

[[covariates]]
label = "employment"
registry_pattern = "akm_path"
column = "SOCIO13"

[[covariates]]
label = "education"
registry_pattern = "/path/to/uddf/*.parquet"
column = "HFAUDD"
```

## Pipeline Cache
To speed repeat runs, cache each phase under `[output]` and enable resume:
```toml
[output]
output_dir = "output"
cache_dir = "output/cache"
resume = true
# clean_cache = true
```

CLI equivalents: `--resume`, `--clean-cache`, `--cache-dir`.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`
epicsdev-simulated 0.1.0
EPICS PVAccess server for Tektronix MSO oscilloscopes
Requires Python: >=3.7

# epicsdev_simulated
EPICS PVAccess servers for simulated devices

Start: ```python -m epicsdev_simulated.simscope -l```<br.
Access:<br>
```
python -m p4p.client.cli get simScope1:cycle
python -m p4p.client.cli monitor simScope1:WaveForm_RBV
```
Plot: ```python -m pvplot -a'V:simScope1:' WaveForm_RBV```
slackclip 1.0.1
Copy the contents of a Slack thread.
Requires Python: >=3.10

# slackclip

Copy the contents of a Slack thread.

---

## Description

`slackclip` extracts a Slack thread, formats it as Markdown, and copies it to the clipboard.

**This project is not endorsed or authorized in any way by Slack Technologies LLC.**

## Installation

Requires Python 3.10+.

```bash
pip install slackclip
```

Or with [uv](https://docs.astral.sh/uv/):

```bash
uv pip install slackclip
```

## Usage

```bash
slackclip <slack-thread-url>
```

By default, output is copied to the clipboard.

### Save to file

```bash
# Auto-generate filename (slack-thread-YYYYMMDD-HHMMSS.md)
slackclip <url> -f

# Specify filename
slackclip <url> -f output.md
```

### Pipe mode

Read URL from stdin, write content to stdout:

```bash
echo "<url>" | slackclip -p
```

## First-time setup

On first run, `slackclip` will prompt you for your Slack credentials:

1. Open Slack in your browser and log in
2. Open Developer Tools (F12 or Cmd+Option+I)
3. Go to **Console** tab and run:
   ```javascript
   JSON.parse(localStorage.localConfig_v2).teams
   ```
4. Find your workspace and copy the `token` value (starts with `xoxc-`)
5. Go to **Application** tab → **Cookies** → `https://app.slack.com`
6. Find the cookie named `d` and copy its value

Credentials are saved to `~/.config/slackclip/credentials` for future use. If they expire, you'll be prompted to enter new ones.

## Output format

Messages are formatted as Markdown:

```markdown
**Jane Doe, at 2024-01-15 14:30:00:**
This is the first message in the thread.

**John Smith, at 2024-01-15 14:32:00:**
This is a reply with a [link](https://example.com) and some **bold text**.
```

## Shortcomings

- Multimedia (images, files) won't come through
- Reactions aren't included
- Only outputs Markdown format

## Credits

Originally created by [Heath Raftery](https://github.com/hraftery/slackclipper).