semvec/pypi-packages huggingface dataset:
threedi-modelchecker 2.18.17
Checks validity of a 3Di schematisation
License: MIT
Requires Python: >=3.9

threedi-modelchecker
====================

.. image:: https://img.shields.io/pypi/v/threedi-modelchecker.svg
        :target: https://pypi.org/project/threedi-modelchecker/

.. image:: https://github.com/nens/threedi-modelchecker/actions/workflows/test.yml/badge.svg
	:alt: Github Actions status
	:target: https://github.com/nens/threedi-modelchecker/actions/workflows/test.yml


A tool to verify the correctness of a 3Di schematisation.

It asserts the correctness of a 3Di schematisation and provides detailed 
information about any potential errors in it.

This package only work against a specific schematisation version. Use https://pypi.org/project/threedi-schema/ to upgrade
a schematisation version. See also the 3Di documentation at https://docs.3di.lizard.net/en/stable/d_before_you_begin.html#database-overview

Note that the name "modelchecker" might be slightly confusing: the thing that is checked is a schematisation, and not
a threedimodel. A schematisation is built interactively by the user and this schematisation will be converted to a
3Di-Model which can be used in simulations.

Installation
------------

    pip install threedi-modelchecker

Note that raster checks will be skipped unless GDAL is available.
``threedi-modelchecker`` is also integrated into the ThreediToolbox Qgis plugin: https://github.com/nens/ThreeDiToolbox

Example
-------

The following code sample shows how you can use the modelchecker to run all configured
checks and print an overview of all discovered errors::

    from threedi_modelchecker.exporters import format_check_results
    from threedi_modelchecker import ThreediModelChecker
    from threedi_schema import ThreediDatabase

    sqlite_file = "<Path to your sqlite file>"
    database = ThreediDatabase(sqlite_file)

    model_checker = ThreediModelChecker(database)
    for check, error in model_checker.errors(level="WARNING"):
        print(format_check_results(check, error))


Command-line interface
----------------------

Use the modelchecker from the command line as follows::

    threedi_modelchecker check -s path/to/model.sqlite -l warning 

By default, WARNING and INFO checks are ignored. To skip the beta features check,
add the --allow-beta flag.


Development
-----------

A docker image has been created for easy development. It contains an postgis 
server with an empty 3Di database to allow for easy testing.

Build the image:

    docker-compose build

Run the tests:

    docker-compose run modelchecker pytest

See `Creating revisions <https://github.com/nens/threedi-schema/blob/master/threedi_schema/migrations/README.rst>`_ for 
instructions on how to change the 3Di model schematisation.

Release
-------

Make sure you have zestreleaser_ installed.

    fullrelease

When you created a tag, make sure to upload it to pypi_.

.. _zestreleaser: https://zestreleaser.readthedocs.io/en/latest/
.. _pypi: https://pypi.org/project/threedi-modelchecker/
polars-cloud 0.5.0
Execute your Polars workloads in the cloud
License: MIT
Requires Python: >=3.10

![Image showing the Polars Cloud logo](https://raw.githubusercontent.com/pola-rs/polars-static/ce84036de7e939a82eb57c88056244c17f35fdba/logos/polars_cloud_logo_blue.svg)

<div align="center">
  <a href="https://pypi.org/project/polars_cloud/">
    <img src="https://img.shields.io/pypi/v/polars_cloud.svg" alt="PyPi Latest Release"/>
  </a>

<a href="https://docs.cloud.pola.rs/reference/index.html">Documentation</a>
|
<a href="https://stackoverflow.com/questions/tagged/polars-cloud">Stack Overflow</a>
|
<a href="https://docs.pola.rs/polars-cloud/">User guide</a>
|
<a href="https://discord.gg/4UfP5cfBE7">Discord</a>

</div>

# Polars Cloud: Run your queries at scale, anywhere

Built on top of the popular open source project, Polars Cloud enables you to write DataFrame code
once and run it anywhere. The distributed engine available with Polars Cloud allows you to scale
your Polars queries beyond a single machine.

## Key Features of Polars Cloud

- **Unified DataFrame Experience**: Run a Polars query seamlessly on your local machine or at scale with our new
  distributed engine. All from the same API.
- **Serverless Compute**: Effortlessly start compute resources without managing infrastructure, with options to run
  queries on both CPU and GPU.
- **Any Environment**: Start a remote query from a notebook on your machine, Airflow DAG, AWS Lambda, or any server.
  Get the flexibility to embed Polars Cloud in any environment.

## Install Polars Cloud

To use Polars cloud simply add it to your existing project

```bash
pip install polars_cloud
```

Then call `.remote()` on your dataframe and provide a compute context.

```python
import polars as pl
import polars_cloud as pc

ctx = pc.ComputeContext(cpus=16, memory=64)

query = (
    pl.scan_parquet("s3://my-dataset/")
    .group_by("returned", "status")
    .agg(
        avg_price=pl.mean("price"),
        avg_disc=pl.mean("discount"),
        count_order=pl.len(),
    )
)

(
    query.remote(ctx)
    .distributed()
    .sink_parquet("s3://my-destination/")
)
```

Hit run and your query will be executed in the cloud. You can follow your query's progress
on [the dashboard](https://cloud.pola.rs/portal/dashboard). And once your first query is done it's time to increase your
dataset size and up the core count.

## Sign up today

[Sign up here](https://cloud.pola.rs/) to run Polars Cloud.