semvec/pypi-packages huggingface dataset:
insillyclo 1.1.0
A toolkit to support Golden Gate assembly, along with digestion, gel simulation, and more
Requires Python: >=3.10

# InSillyClo-cli

Documentation available at http://insillyclo.pages.pasteur.fr/insillyclo-cli

## Install

```shell
#!/bin/bash

virtualenv .venv
# alternative:
# python3 -m venv .venv
source .venv/bin/activate
pip install insillyclo
# alternative:
# pip install insillyclo --index-url https://gitlab.pasteur.fr/api/v4/projects/6917/packages/pypi/simple
```

## Use it

### Get the test data

```shell
git clone git@gitlab.pasteur.fr:hub/insillyclo-cli.git
cd insillyclo-cli/
```

### ... to generate a template

```shell
insillyclo template my-template.xlsx \
                    --name "My template" \
                    --restriction-enzyme-goldengate "BsmBI" \
                    --separator - \
                    --nb-input-parts 4
```

or

```shell
insillyclo template my-template.xlsx \
                    --name "My template" \
                    --enzyme "BsmBI" \
                    --separator - \
                    --input-part ConL \
                    --input-part Promoter \
                    --input-part CDS \
                    --input-part Terminator \
                    --input-part ConR \
                    --input-part Backbone
```

### ... run simulation

```shell
insillyclo simulate --input-template-filled ./tests/data/template_01_mixed_ok.xlsx \
                    --input-parts-file ./tests/data/DB_iP_not_typed.csv \
                    --input-parts-file ./tests/data/DB_iP_typed.csv \
                    --plasmid-repository ./tests/data/plasmids_gb \
                    -o ./output/ \
                    --restriction-enzyme-gel NotI \
                    --primer-pair P84,P134 \
                    --primers-file ./tests/data/primers.csv \
                    --default-mass-concentration 200 \
                    --enzyme-and-buffer-volume 1.0
```
fp-quant 0.3.2
A Python library for FP-Quant
Requires Python: >=3.9

# fp_quant

A library that wraps [`qutlass`](https://github.com/IST-DASLab/qutlass) kernels with linear layer wrappers for integrations into training and inference engines.

## Installation

```bash
pip install .
```

## Usage

```python
from fp_quant import replace_with_fp_quant_linear, FPQuantConfig

# Replace nn.Linear layers with fp_quant.FPQuantLinear
replace_with_fp_quant_linear(
    model,
    fp_quant_linear_config=FPQuantConfig(),
)
```
enclave 0.3.4
Connect to Enclave Markets API
Requires Python: >=3.8

# Enclave Python

<p align="center">
    <a href="https://github.com/Enclave-Markets/enclave-python" alt="enclave python">
        <img src="https://edent.github.io/SuperTinyIcons/images/svg/github.svg" width="50" /></a>
    <a href="https://pypi.org/project/enclave/">
        <img src="https://upload.wikimedia.org/wikipedia/commons/0/04/PyPI-Logo-notext.svg" width="50"/></a>
    <a href="https://twitter.com/enclavemarkets" alt="Enclave Twitter">
        <img src="https://edent.github.io/SuperTinyIcons/images/svg/x.svg" width="50"/></a>
    <a href="https://www.enclave.market/" alt="Enclave Market">
        <img src="https://pbs.twimg.com/profile_images/1650572649284931585/rbv_Z4Lr_400x400.jpg" width="50"/></a>
        
</p>

This is the official Python SDK for
[Enclave Markets](https://enclave.market/).

It provides a simple interface for interacting with the
[Enclave API](https://docs.enclave.market/).

Find our easy [Colab Demo](https://colab.research.google.com/drive/1Op5v4GuJozd6UvS2c7cXV0r6hswl4SVv) here.

## Installation

```bash
pip install enclave
```

## Usage

```python
from enclave.client import Client
import enclave.models

client = Client("", "", enclave.models.PROD)
print(client.wait_until_ready()) # should print True
```

### Perps Order

```python
buy_order = client.perps.add_order(
    client.perps.OrderParams(
        market="BTC-USD.P",
        side=enclave.models.BUY,
        price=Decimal(42_000),
        size=Decimal(0.1),
        order_type=enclave.models.LIMIT
    )
)
```

## Examples

See the [examples](examples) directory for more examples.

Rest API examples can be found in [intro.py](examples/intro.py).
Run from the root directory with `python -m examples.intro`.

Websocket API examples can be found in [wsintro.py](examples/wsintro.py).
Run from the root directory with `python -m examples.wsintro`.

Hosted [demo on Google Colab](https://colab.research.google.com/drive/1Op5v4GuJozd6UvS2c7cXV0r6hswl4SVv) for ease of use, including expected output.

## Support

Supports Python 3.8+.
cartes 0.8.5
A generic toolbox for building maps in Python
Requires Python: >=3.10

# Cartes

![tests](https://github.com/xoolive/cartes/workflows/tests/badge.svg)
![docs](https://github.com/xoolive/cartes/workflows/docs/badge.svg)
![Code Coverage](https://img.shields.io/codecov/c/github/xoolive/cartes.svg)
![Checked with mypy](https://img.shields.io/badge/mypy-checked-blue.svg)
![Code style: black](https://img.shields.io/badge/code%20style-black-black.svg)
![License](https://img.shields.io/pypi/l/cartes.svg)\
![PyPI version](https://img.shields.io/pypi/v/cartes)
![PyPI downloads](https://img.shields.io/pypi/dm/cartes)
![Conda version](https://img.shields.io/conda/vn/conda-forge/cartes)
![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/cartes.svg)

Cartes is a Python library providing facilities to produce meaningful maps.

Cartes builds on top of most common Python visualisation libraries (Matplotlib/Cartopy, Altair, ipyleaflet) and data manipulation libraries (Pandas, Geopandas) and provides mostly:

- a **comprehensive set of geographic projections**, built on top of Cartopy and Altair/d3.js;
- an **interface to OpenstreetMap Nominatim and Overpass API**. Result of requests are parsed in a convenient format for preprocessing and storing in standard formats;
- beautiful **default parameters** for quality visualisations;
- **advanced caching facilities**. Do not download twice the same content in the same day.

The cartes library is a powerful asset to **publish clean, lightweight geographical datasets**; and to **produce decent geographical visualisations** in few lines of code.

## Gallery

<a href="https://cartes-viz.github.io/gallery/mercantour.html"><img width="20%" src="https://cartes-viz.github.io/_static/homepage/mercantour.png"></a>
<a href="https://cartes-viz.github.io/gallery/footprint.html"><img width="20%" src="https://cartes-viz.github.io/_static/homepage/antibes.png"></a>
<a href="https://cartes-viz.github.io/gallery/airports.html"><img width="20%" src="https://cartes-viz.github.io/_static/homepage/airports.png"></a>
<a href="https://cartes-viz.github.io/gallery/tokyo_metro.html#zoom-in-to-downtown-tokyo"><img width="20%" src="https://cartes-viz.github.io/_static/homepage/tokyo.png"></a>

More in the [documentation](https://cartes-viz.github.io/gallery.html)

## Installation

Latest release:

Recommended for beginners, with conda:

```sh
conda install -c conda-forge cartes
```

or with pip:

```sh
pip install cartes
```

Development version, with uv:

```sh
git clone https://github.com/xoolive/cartes
cd cartes
uv sync --dev
```

## Documentation

![docs](https://github.com/xoolive/cartes/workflows/docs/badge.svg)

Documentation available at https://cartes-viz.github.io/