semvec/pypi-packages huggingface dataset:
sadcompressor 0.1.1
Streamed Array Data compressor
Requires Python: >=3.8
Keywords: compression, archive, stream

# sadcompressor

`sadcompressor` is a compact archival format and Python library for streamed time-series data (mainly NumPy arrays). It stores frames with delta updates, optional prediction, and quantization-based compression.

## Quick Start

```python
import numpy as np
import sadcompressor as sad

filename = "example.sad"

# Create archive
with sad.SADWriter(filename, prec_nbits=20, prec_maxexp=8) as writer:
    writer["x"] = np.array([1.0, 2.0, 3.0], dtype=np.float32)
    writer.next_key(0.1)
    writer["x"] = np.array([1.1, 2.1, 3.1], dtype=np.float32)

# Read archive sequentially
with sad.SADReader(filename) as reader:
    while not reader.next_key():
        print(f"t={reader.t:.3f}", reader["x"])
```
policyengine-uk 2.73.1
PolicyEngine tax and benefit system for the UK.
Requires Python: <3.14,>=3.13
Keywords: benefit, microsimulation, social, tax

# PolicyEngine UK

[![PyPI version](https://badge.fury.io/py/policyengine-uk.svg)](https://badge.fury.io/py/policyengine-uk)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

PolicyEngine UK is [PolicyEngine](https://policyengine.org)'s microsimulation model of the UK tax-benefit system.
It uses the PolicyEngine Core microsimulation framework, which is based on [OpenFisca](https://openfisca.org). 

The elements are described in different folders. All the modelling happens within the `policyengine_uk` folder.

- The rates and other system parameters are in the `parameters` folder.
- The formulas and inputs are in the `variables` folder.
- This country package comes also with reforms in the `reforms` folder.

The files that are outside from the `policyengine_uk` folder are used to set up the development environment. Installation instructions are located along with other documentation in the `docs` folder.

The model supports multiple different input datasets provided by the user, one of which is the Family Resources Survey,[^1] containing microdata on household incomes across the UK.
PolicyEngine UK enhances this dataset by fusing it to other surveys and reweighting it to minimize a comprehensive loss metric that measures the difference from an array of administrative totals.

[^1]: Department for Work and Pensions, Office for National Statistics, NatCen Social Research. (2021). Family Resources Survey, 2019-2020. [data collection]. UK Data Service. SN: 8802, http://doi.org/10.5255/UKDA-SN-8802-1


## Fast setup instructions

1. Run `pip install policyengine-uk`

2. Run `policyengine-uk` and go through the prompt to setup microdata.


## Contact

The primary maintainer for PolicyEngine UK is Nikhil Woodruff, co-founder and CTO of PolicyEngine (nikhil@policyengine.org).


## Citation

You may cite the source of your analysis as "PolicyEngine UK release #.#.#, author's calculations."
apple-mail-mcp 0.1.2
Fast MCP server for Apple Mail with FTS5 search index
Requires Python: >=3.11
Keywords: apple-mail, apple-mail-mcp, automation, email, fts5, macos, mcp, model-context-protocol

# Apple Mail MCP

<!-- mcp-name: io.github.imdinu/apple-mail-mcp -->

[![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![macOS](https://img.shields.io/badge/platform-macOS-lightgrey.svg)](https://www.apple.com/macos/)
[![MCP](https://img.shields.io/badge/MCP-compatible-green.svg)](https://modelcontextprotocol.io/)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)
[![CI](https://github.com/imdinu/apple-mail-mcp/actions/workflows/lint.yml/badge.svg)](https://github.com/imdinu/apple-mail-mcp/actions/workflows/lint.yml)

A fast MCP server for Apple Mail — **87x faster** email fetching via batch JXA, plus an FTS5 search index for **700–3500x faster** body search (~2ms vs ~7s).

**[Read the docs](https://imdinu.github.io/apple-mail-mcp/)** for the full guide.

## Quick Start

```bash
pipx install apple-mail-mcp
```

Add to your MCP client:

```json
{
  "mcpServers": {
    "mail": {
      "command": "apple-mail-mcp"
    }
  }
}
```

### Build the Search Index (Recommended)

```bash
# Requires Full Disk Access for Terminal
# System Settings → Privacy & Security → Full Disk Access → Add Terminal

apple-mail-mcp index --verbose
```

## Tools

| Tool | Purpose |
|------|---------|
| `list_accounts()` | List email accounts |
| `list_mailboxes(account?)` | List mailboxes |
| `get_emails(filter?, limit?)` | Get emails — all, unread, flagged, today, this_week |
| `get_email(message_id)` | Get single email with full content |
| `search(query, scope?)` | Search — all, subject, sender, body |

## Performance

| Scenario | Apple Mail MCP | Best alternative | Speedup |
|----------|---------------|-----------------|---------|
| Fetch 50 emails | 529ms | 15,288ms | **29x** |
| Body search | ~2ms | ~7,000ms (or unsupported) | **3500x** |
| List accounts | 108ms | 146ms | Fastest |

> Benchmarked against [7 other Apple Mail MCP servers](https://imdinu.github.io/apple-mail-mcp/benchmarks/) at the MCP protocol level.

## Configuration

| Variable | Default | Description |
|----------|---------|-------------|
| `APPLE_MAIL_DEFAULT_ACCOUNT` | First account | Default email account |
| `APPLE_MAIL_DEFAULT_MAILBOX` | `INBOX` | Default mailbox |
| `APPLE_MAIL_INDEX_PATH` | `~/.apple-mail-mcp/index.db` | Index location |

```json
{
  "mcpServers": {
    "mail": {
      "command": "apple-mail-mcp",
      "args": ["--watch"],
      "env": {
        "APPLE_MAIL_DEFAULT_ACCOUNT": "Work"
      }
    }
  }
}
```

## Development

```bash
git clone https://github.com/imdinu/apple-mail-mcp
cd apple-mail-mcp
uv sync
uv run ruff check src/
uv run pytest
```

## License

GPL-3.0-or-later
hydra-sweeper-explicit 0.0.1
Hydra sweeper for explicit parameter combinations without Cartesian product
Requires Python: >=3.12
Keywords: hydra, hyperparameter, machine-learning, sweeper

# hydra-sweeper-explicit

[![Tests][badge-tests]][tests]
[![PyPI][badge-pypi]][pypi]

[badge-tests]: https://img.shields.io/github/actions/workflow/status/quadbio/hydra-sweeper-explicit/test.yaml?branch=main&label=tests
[badge-pypi]: https://img.shields.io/pypi/v/hydra-sweeper-explicit

[tests]: https://github.com/quadbio/hydra-sweeper-explicit/actions/workflows/test.yaml
[pypi]: https://pypi.org/project/hydra-sweeper-explicit

A Hydra sweeper for running explicit parameter combinations without Cartesian product.

## Installation

```bash
pip install hydra-sweeper-explicit
```

## Usage

```yaml
hydra:
  sweeper:
    _target_: hydra_sweeper_explicit.ExplicitSweeper
    combinations:
      - {model: small, lr: 0.01}
      - {model: large, lr: 0.001}
      - {model: large, lr: 0.0001, dropout: 0.5}
```

```bash
python train.py --multirun
```

Runs exactly 3 jobs—no Cartesian product.
duplocloud-client 0.4.1
Command line Client for interacting with Duplocloud portals.
Requires Python: >=3.10.0
Keywords: duplocloud, duplo, duploctl, duplo-client

# Duplocloud Py Client

[![Unit Tests](https://github.com/duplocloud/duploctl/actions/workflows/test_unit.yml/badge.svg)](https://github.com/duplocloud/duploctl/actions/workflows/test_unit.yml) [![PyPI - Version](https://img.shields.io/pypi/v/duplocloud-client?logo=pypi)](https://pypi.org/project/duplocloud-client/) [![Docker Image Version](https://img.shields.io/docker/v/duplocloud/duploctl?sort=semver&logo=Docker&label=docker&color=blue&link=https%3A%2F%2Fhub.docker.com%2Fr%2Fduplocloud%2Fduploctl)](https://hub.docker.com/r/duplocloud/duploctl) [![GitHub Release](https://img.shields.io/github/v/release/duplocloud/duploctl?logo=github&label=Github&color=purple)
](https://github.com/duplocloud/duploctl) [![Static Badge](https://img.shields.io/badge/Docs-lightblue?logo=github)
](https://cli.duplocloud.com/)

```duploctl``` is a cli and package to work with a Duplocloud portal. It is a CLI for interacting with Duplocloud resources, such as Tenants, and is designed to work seamlessly within CLI-based CI/CD pipelines. It is a fully extensible package and can be used as both a Python module and a CLI.

## Installation

From PyPi:

```sh
pip install duplocloud-client
```

From Homebrew:

```sh
brew install duplocloud/tap/duploctl
```

## Usage

Use ```duploctl``` as a CLI or as a standalone Python module called by your custom script.

### Configuration

Use the following syntax for these global arguments:

| Arg | Env Var | Description | Default | Required |
| --- | ------- | ----------- | ------- | -------- |
| --host, -H | DUPLO_HOST | The host to connect to |  | Yes |
| --token, -T | DUPLO_TOKEN | The token to use for auth |  | Yes |
| --tenant, -t | DUPLO_TENANT | The tenant to use for auth | default | No |

### CLI

CLI command syntax for invoking ```duploctl```

```sh
duploctl <resource> <command> <args...>
```

### Example Usages

Full documentation is in the Wiki section.

Configure `duploctl` access with environment variables:

```sh
export DUPLO_HOST=https://example.duplocloud.net
export DUPLO_TOKEN=AQAAA...
export DUPLO_TENANT=dev01
```

List the services in a tenant:

```sh
duploctl service list
```

Register Profile for AWS:

```sh
duploctl jit update_aws_config myportal
```

Open AWS Web Console:

```sh
duploctl jit web
```

Get Kubernetes config:

```sh
duploctl jit update_kubeconfig myinfra
```

### Python Module

Spawn your client from a Python script using the ```DuploClient.from_env()``` method and arguments. The second return value are the unparsed arguments from the command line. This example uses the client as a callable using command like syntax.

```python
duplo, args = DuploClient.from_env()
t = duplo("tenant", "find", "mytenant")
print(t)
```

Spawn a client with a custom host and token from a Python script. This example loads a resource and runs a method manually.

```python
duplo = DuploClient.from_creds(host="https://example.duplocloud.net", token="mytoken")
tenants = duplo.load("tenant")
t = tenants.find("mytenant")
print(t)
```
hindsight-all 0.4.13
Hindsight: Agent Memory That Works Like Human Memory - All-in-One Bundle
Requires Python: >=3.11

# hindsight-all

All-in-one package for Hindsight - Agent Memory That Works Like Human Memory

## Quick Start

```python
from hindsight import start_server, HindsightClient

# Start server with embedded PostgreSQL
server = start_server(
    llm_provider="groq",
    llm_api_key="your-api-key",
    llm_model="openai/gpt-oss-120b"
)

# Create client
client = HindsightClient(base_url=server.url)

# Store memories
client.put(agent_id="assistant", content="User prefers Python for data analysis")

# Search memories
results = client.search(agent_id="assistant", query="programming preferences")

# Generate contextual response
response = client.think(agent_id="assistant", query="What languages should I recommend?")

# Stop server when done
server.stop()
```

## Using Context Manager

```python
from hindsight import HindsightServer, HindsightClient

with HindsightServer(llm_provider="groq", llm_api_key="...") as server:
    client = HindsightClient(base_url=server.url)
    # ... use client ...
# Server automatically stops
```

## Installation

```bash
pip install hindsight-all
```
fw-storage 3.8.6
Unified storage interface.
Requires Python: >=3.11
Keywords: Flywheel, file, object, storage

# fw-storage

Unified file storage interface tuned for simple filtering, memory-efficiency and
performance to support processing large datasets in Flywheel imports and exports.

Supported storage backends:

- `fs://` - Local file-system
- `s3://` - Amazon S3
- `gs://` - Google Cloud Storage
- `az://` - Azure Blob Storage

## Installation

Add as a `poetry` dependency to your project:

```bash
poetry add fw-storage
```

## Usage

```python
from fw_storage import create_storage_client

# instantiate storage with URL
fs = create_storage_client("fs:///tmp")

# set objects from bytes, filepaths or open files
fs.set("test/file1.dat", b"content")
fs.set("test/file2.dat", "/tmp/test/file1.dat")
fs.set("test/file3.dat", open("/tmp/test/file2.dat"))

# list objects, filtering with expressions
files = list(fs.ls("test", include=["size<1kB"], exclude=["path!~file3"]))
len(files) == 2

# get object info with path, size, created and modified
info = fs.stat("test/file1.dat")
info.size == 7

# read object contents
file = fs.get("test/file1.dat")
file.read() == b"content"

# remove one or more objects
fs.rm("test", recurse=True)
```

## Configuration

Credentials for cloud storage providers are loaded using the vendor SDKs to
support every standard config file location and environment variable recommended
by the provider:

| Storage | Config docs           |
| ------- | --------------------- |
| `s3://` | [Boto][boto-docs]     |
| `gs://` | [Google][google-docs] |
| `az://` | [Azure][azure-docs]   |

In addition, `az://` can be configured with the envvar `AZ_ACCESS_KEY`.

[boto-docs]: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
[google-docs]: https://google-auth.readthedocs.io/en/latest/reference/google.auth.html
[azure-docs]: https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential

## Development

Install the project using `poetry` and enable `pre-commit`:

```bash
poetry install -E all
pre-commit install
```

## License

[![MIT](https://img.shields.io/badge/license-MIT-green)](LICENSE)