semvec/pypi-packages huggingface dataset:
physioblocks 1.1.3
Physioblocks allows the simulation of dynamical models of physiological systems
Requires Python: >=3.10
Keywords: block diagram, cardiovascular modeling, dynamical systems, physiology

# PhysioBlocks

PhysioBlocks allows the simulation of dynamical models of physiological systems.

## User Levels

They are several use cases for PhysioBlocks depending on the user profile:

* __Level 1:__ Configure and run physiological systems simulation (for pre-existing systems)
* __Level 2:__ Create new systems with existing blocks without writing code
* __Level 3:__ Write and add new blocks to the library.

## Principle

* A __Net__ (system) is built from __Nodes__ and __Blocks__ connected by those nodes.
* At each node in the net, connected blocks share __Degrees of Freedom__ (ex: pressure) and send __Fluxes__ that verify Kirchhoff Law.
* __ModelComponents__ concatenate blocks equations to the global system (if necessary, for modularity purposes within the block)

## Interactions

__Level 1:__ Configure and run a simulation : JSON
* Update the model parameters

__Level 2:__ Create Nets : JSON
* Declare the nodes, the blocks, and the block - nodes connections


__Level 3:__ Write and add models to the library: Python
* Declare the quantities to use in the model
* Write the fluxes and equations

## Documentation

Here are the links to the sections of the [full documentation](https://physioblocks.gitlabpages.inria.fr/physioblocks/):
* [Installation](https://physioblocks.gitlabpages.inria.fr/physioblocks/installation.html)
* [User Guide](https://physioblocks.gitlabpages.inria.fr/physioblocks/user_guide.html)
* [Library](https://physioblocks.gitlabpages.inria.fr/physioblocks/library.html)
* [API Reference](https://physioblocks.gitlabpages.inria.fr/physioblocks/api_reference.html)

## Quick start

Complete instructions are available in the [documentation](https://physioblocks.gitlabpages.inria.fr/physioblocks/). This instructions will enable you to launch a reference simulation.

### Installation

This project requires a recent version of python installed.
Then: 
```
    pip install physioblocks
```

### Configuration

To configure PhysioBlocks Launcher:

```
# Create an empty folder where you want to store simulations results.
mkdir $LAUNCHER_FOLDER_PATH$

# Configure the folder
python -m physioblocks.launcher.configure -d $LAUNCHER_FOLDER_PATH$ -v
```

### Launch a simulation

With a Launcher folder configured:

```
# Move to your configured launcher folder
cd $LAUNCHER_FOLDER_PATH$

#  Launch a reference simulation
python -m physioblocks.launcher references/spherical_heart_sim.jsonc -v -t -s QuickStart

# This can take some time.
```


Results will be available in the `$LAUNCHER_FOLDER_PATH$/simulations/QuickStart` series folder:
* the csv file contains the simulation results.
* the html allows you to visualize the results.
* the log and json files are here for debug purposes.
db-contrib-tool 2.2.3
The `db-contrib-tool` - MongoDB's tool for contributors.
Requires Python: <3.13,>=3.9

# db-contrib-tool

The `db-contrib-tool` - a collection of tooling to support repro environments.

## Description

The command line tool with various subcommands:
- `setup-repro-env`
  - [README.md](src/db_contrib_tool/setup_repro_env/README.md)
  - downloads and installs:
    - particular MongoDB versions
    - debug symbols
    - artifacts (including resmoke, python scripts etc)
    - python venv for resmoke, python scripts etc
- `setup-mongot-repro-env`
  - [README.md](src/db_contrib_tool/setup_mongot_repro_env/README.md)
  - Downloads and installs particular Mongot versions into install directory.
- `symbolize`
  - [README.md](src/db_contrib_tool/symbolizer/README.md)
  - Symbolizes stacktraces from recent `mongod` and `mongos` binaries compiled in Evergreen, including patch builds, mainline builds, and release/production builds.
  - Requires authenticating to an internal MongoDB symbol mapping service.

## Dependencies

- Python 3.9 or later (python3 from the [MongoDB Toolchain](https://github.com/10gen/toolchain-builder/blob/master/INSTALL.md) is highly recommended)

## Installation

Make sure [dependencies](#dependencies) are installed.
Use [pipx](https://pypa.github.io/pipx/) to install db-contrib-tool that will be available globally on your machine:

```bash
python3 -m pip install pipx
python3 -m pipx ensurepath
```

Installing db-contrib-tool:

```bash
python3 -m pipx install db-contrib-tool
```

Upgrading db-contrib-tool:

```bash
python3 -m pipx upgrade db-contrib-tool
```

In case of installation errors, some of them may be related to pipx and could be fixed by re-installing pipx.

Removing pipx completely (WARNING! This will delete everything that is installed and managed by pipx):

```bash
python3 -m pip uninstall pipx
rm -rf ~/.local/pipx  # in case you're using the default pipx home directory
```

Now you can try to install again from scratch.

## Usage

Print out help message:

```bash
db-contrib-tool --help
```

Download and install the v8.0 mongo binary:
```sh
db-contrib-tool setup-repro-env 8.0
```

Download other accompanying artifacts with flags:
```sh
db-contrib-tool setup-repro-env 8.0 \
  --downloadSymbols \
  --downloadArtifacts \
  --downloadPythonVenv
```

Download latest binary from the 10gen/mongo master branch:
```sh
db-contrib-tool setup-repro-env master
```

There are more ways to specify binary versions - see [setup_repro_env/README.md](src/db_contrib_tool/setup_repro_env/README.md).

# Contributing

See [./CONTRIBUTING.md](./CONTRIBUTING.md) on details for development on this project.
beancount-format 0.1.26
Beancount formatter powered by dprint-plugin-beancount
Requires Python: >=3.10

# beancount-format

beancount formatter.

## Install

```bash
pip install beancount-format
```

## Usage

### as CLI

```bash
beancount-format ./
```

### as library

```python
from bean_format import format_text

source = "2010-01-01 open Assets:Cash\n"
formatted = format_text(source)
print(formatted)

# Override formatter options
formatted = format_text(
    source,
    path="ledger.beancount",  # defaults to "<memory>"
    line_width=88,
    indent_width=2,
    new_line="lf",  # "lf" or "crlf"
)
print(formatted)
```
milvusmodel.py 0.2.1b0
SQLModel-like library for Milvus
Requires Python: >=3.11

# PyMilvusModel

Inspired by [SQLModel](https://sqlmodel.tiangolo.com/), PyMilvusModel:

> is a library for interacting with [Milvus](https://milvus.io/) databases from Python code, with Python objects.

and follows the same paradigm, using Python type annotations and powered by [Pydantic](https://docs.pydantic.dev/latest/)

## Installation

```sh
pip install milvusmodel.py
```

## Example Usage

```py
from typing import List, Optional
from typing_extensions import Annotated
from pymilvus import MilvusClient, DataType
from pymilvusmodel import MilvusIndexParam, MilvusField, MilvusModel


class ExampleModel(MilvusModel):
    indexes: list[MilvusIndexParam] = [
        MilvusIndexParam("vector", "IVF_FLAT", "vector_index", metric_type="COSINE", params={
            "nlist": 128
        })
    ]
    id: Annotated[
        Optional[int],
        MilvusField(name="id", dtype=DataType.INT64,
                    is_primary=True, auto_id=True)
    ] = None
    vector: Annotated[
        List[float],
        MilvusField(name="vector", dtype=DataType.FLOAT_VECTOR, dim=2)
    ]


MILVUS_CLIENT = MilvusClient("http://localhost:19530")
MilvusModel.metadata.create_all(MILVUS_CLIENT)
print(MILVUS_CLIENT.list_collections()) # ['ExampleModel']
ExampleModel.insert(ExampleModel(vector=[0, 1]))
print(ExampleModel.query(filter="id>=0")) # [ExampleModel(indexes=[<pymilvusmodel.index.MilvusIndexParam object at 0x105c825d0>], id=456328785400861845, vector=[0.0, 1.0])]
```