semvec/pypi-packages huggingface dataset:
cortical-tools 0.0.7
Client for cortical CAVE datasets
Requires Python: >=3.10

# Cortical Tools

Cortical Tools offers dataset-specific Python clients for interacting with several CAVE datasets, in particular the Microns65 dataset and V1dd dataset.
It wraps CAVEclient and related tooling with a more task-focused design than the infrastructure-focused CAVEclient.
The public entry point is a preconfigured client for datasets.

- Get started with the [Examples](http://www.csdashm.com/cortical-tools/examples/)

## Who can use it?

Cortical Tools can be used if you are working with either the publicly accessible datastacks `minnie65_public` or `v1dd_public`.
In addition, if you are using the production datastacks for these same datastacks, you can use those as well.

## Installation

```bash
pip install cortical-tools
```

(Really, you should probably be using [`uv`](https://docs.astral.sh/uv) and doing `uv add cortical-tools` to your project.)

Cortical Tools is opinionated, and installs not only CAVEclient, but a number of related tools like `nglui`, `pcg_skel`, `cloudvolume`, and `standard_transform` that are commonly used in conjunction with CAVE datasets.

## Quick start

```python
from cortical_tools.datasets.minnie_public import client
client
```

This will return a `MinniePublicClient` object that is ready to use. For example, `client.neuroglancer_url()` will return a URL that opens the Neuroglancer viewer at the specified location.
Your standard `CAVEclient` functionality is available under `client.cave`.

## Who made this?

This is a totally unofficial tool that [Casey Schneider-Mizell](https://www.csdashm.com) made to use for himself, and maybe you'll find some useful things here too.
testcontainers-iris 1.3.0
InterSystems IRIS component of testcontainers-python.
Requires Python: >=3.7

# Testcontainers-python for InterSystems IRIS

[testcontainers-python](https://testcontainers-python.readthedocs.io/en/latest/README.html) facilitates the use of Docker containers for functional and integration testing.

## Other implementations

* [testcontainers-iris-java](https://github.com/caretdev/testcontainers-iris-java)

## Basic usage

```
>>> from testcontainers.iris import IRISContainer
>>> import sqlalchemy

>>> iris_container = IRISContainer("intersystemsdc/iris-community:latest")
>>> with iris_container as iris:
...     engine = sqlalchemy.create_engine(iris.get_connection_url())
...     with engine.begin() as connection:
...         result = connection.execute(sqlalchemy.text("select $zversion"))
...         version, = result.fetchone()
>>> version
'IRIS for UNIX (Ubuntu Server LTS for ARM64 Containers) 2023.2 (Build 227U) Mon Jul 31 2023 17:43:25 EDT'
```

The snippet above will spin up a InterSystems IRIS database in a container. The get_connection_url() convenience method returns a sqlalchemy compatible url we use to connect to the database and retrieve the database version.

It is possible to run Enterprise edition as well, with passing a valid iris.key.

```python
iris_container = IRISContainer(
    "containers.intersystems.com/intersystems/iris:2023.3", 
    license_key="/full/path/to/iris.key",
    username="testuser",
    password="testpass",
    namespace="TEST",
)

```

`username`, `password`, `namespace` will be created during start of the container with required values, user will get %All role

[Demo](iris.ipynb) with Jupiter Notebook 

More extensive documentation can be found at [Read The Docs](https://testcontainers-python.readthedocs.io/).
addok-sqlite-store 1.1.0
Store documents in SQLite for Addok.
Requires Python: >=3.9
Keywords: addok, geocoding, sqlite, plugin

# addok-sqlite-store

Addok plugin to store documents in SQLite instead of Redis to reduce memory usage.

## Features

- **SQLite storage**: Store documents in a SQLite database instead of Redis
- **Memory optimization**: Reduce Redis RAM usage for large datasets

## Installation

```bash
pip install addok-sqlite-store
```

## Configuration

Add the following to your Addok configuration file to activate the plugin:

```python
# Use SQLite as document store
DOCUMENT_STORE_PYPATH = 'addok_sqlite_store.SQLiteStore'
```

The SQLite database will be created at `addok.db` by default. You can customize the path:

```python
# Optional: customize the database path
SQLITE_DB_PATH = '/path/to/your/database.db'
```

Or use environment variables:

```bash
export ADDOK_DOCUMENT_STORE_PYPATH='addok_sqlite_store.SQLiteStore'
export ADDOK_SQLITE_DB_PATH='/path/to/your/database.db'  # optional
```