semvec/pypi-packages huggingface dataset:
tink-python-api-types 0.0.13
Python types for the tink API
Requires Python: <=3.11,>=3.8

# tink-python-api-types
[![tag](https://img.shields.io/github/tag/namelivia/tink-python-api-types.svg)](https://github.com/namelivia/tink-python-api-types/releases) [![build](https://github.com/namelivia/tink-python-api-types/actions/workflows/build.yml/badge.svg)](https://github.com/namelivia/tink-python-api-types/actions/workflows/build.yml) [![codecov](https://codecov.io/gh/namelivia/tink-python-api-types/branch/main/graph/badge.svg?token=NlsLE3qD4V)](https://codecov.io/gh/namelivia/tink-python-api-types)
 [![Lint](https://github.com/namelivia/tink-python-api-types/actions/workflows/black.yml/badge.svg)](https://github.com/namelivia/tink-python-api-types/actions/workflows/black.yml)

## About
Python types for the Tink API entities.

## Installation

This package is hosted on [PyPi](https://pypi.org/project/tink-python-api-types), you can install it using any python package manager. 

```bash
$ pip install tink-python-api-types
```

## License

[MIT](LICENSE)

## Contributing
Any suggestion, bug reports, prs or any other kind enhacements are welcome. Just [open an issue first](https://github.com/namelivia/tink-python-api-types/issues/new), for creating a PR remember this project has linting checkings and unit tests so any PR should comply with both before beign merged, this checks will be automatically applied when opening or modifying the PRs.

## Local development

This project comes with a `docker-compose.yml` file so if you use Docker and docker-compose you can develop without installing anything on your local environment. Just run `docker-compose up --build` for the first time to setup the container and launch the tests. Pytest is configured as the entrypoint so just run `docker-compose up` everytime you want the tests to execute on the Dockerized Python development container.
grid-apps 0.1.0
Grid creation and manipulation using GEOH5 format.
Requires Python: <4.0,>=3.10
Keywords: geology, geophysics, earth sciences

grid-apps
=========

The **grid-apps** library offers tools for the creation and manipulation of grid objects (Octree, BlockModel, Grid2D, DrapeModel) using various Python libraries and `geoh5py <https://mirageoscience-geoh5py.readthedocs-hosted.com/>`_.

Installation
^^^^^^^^^^^^
**grid-apps** is currently written for Python 3.10 or higher.

Install **grid-apps** from PyPI::

    $ pip install grid-apps


Feedback
^^^^^^^^
Have comments or suggestions? Submit feedback.
All the content can be found on the github_ repository.

.. _github: https://github.com/MiraGeoscience/grid-apps


Visit `Mira Geoscience website <https://mirageoscience.com/>`_ to learn more about our products
and services.


License
^^^^^^^
MIT License

Copyright (c) 2024-2025 Mira Geoscience

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Third Party Software
^^^^^^^^^^^^^^^^^^^^
The grid-apps Software may provide links to third party libraries or code (collectively “Third Party Software”)
to implement various functions. Third Party Software does not comprise part of the Software.
The use of Third Party Software is governed by the terms of such software license(s).
Third Party Software notices and/or additional terms and conditions are located in the
`THIRD_PARTY_SOFTWARE.rst`_ file.

.. _THIRD_PARTY_SOFTWARE.rst: docs/source/THIRD_PARTY_SOFTWARE.rst

Copyright
^^^^^^^^^
Copyright (c) 2024-2025 Mira Geoscience Ltd.
ezicon 1.2.0
Create Windows Start Menu shortcuts for Python scripts instantly.

# ezicon

A simple tool to create Windows Start Menu shortcuts for Python scripts.

## Installation
```bash
pip install ezicon
```

## Usage

### Basic Usage
Create a shortcut for your script in the Start Menu:
```bash
ezicon my_script.py
```

### Custom Name
Specify a custom name for the shortcut:
```bash
ezicon my_script.py "My Awesome App"
```

### Clean Up
Remove all shortcuts created by `ezicon`:
```bash
ezicon --clean
```

After creating the program icon, you can execute your python script from the Start Menu.
cdef-pil-070126 0.1.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, &registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`
cdef-pil-060126 2.0.0
Parental income loss analysis pipeline.
Requires Python: >=3.9

# Parental Income Loss

## Stacked Event-Study Outputs
The stacked example writes results to:
- `output/stacked_event_study/stacked_results.csv`
- `output/stacked_event_study/stacked_results.parquet`

Each row contains:
- `sample`, `chapter`, `component` (event_time or horizon_*),
- `reference_year`, `target_year`, `event_time`,
- `att`, `se`, `conf_low`, `conf_high`,
- `n_pairs`, `n_treated`, `n_control`, `convergence`,
- `bucket_periods`, `horizon_start`, `horizon_end`, `horizon_label`, `bucket_weight`.

Bucket weighting defaults to treated-weighted (`n_treated`). Override with:
- `STACKED_BUCKET_WEIGHT=pairs`

## Quickstart: Custom Components
- Implement one or more of: `PopulationAssembler`, `CohortSelector`, `ExposureDetector`, `MatchingEngine`.
- Wire them into the pipeline with `CohortWorkflow::with_components`.
- Use `docs/component_contracts.md` to ensure required columns are present.

Example wiring:
```rust
let workflow = CohortWorkflow::with_components(builder, selector, matcher);
let outputs = workflow.run(sources, &detector, &registries, &plan, None)?;
```

## Stacked Event Study CLI
- Run the standalone stacked event-study on a parquet file:
  `cargo run -- --stacked-input path/to/stacked_inputs.parquet --did-outcome perindkialt13`
- Optional flags: `--stacked-samples`, `--stacked-chapter-col`, `--stacked-icd-col`,
  `--stacked-output-dir`, plus the existing `--stacked-*` config flags.

## Docs
- Component contracts: `docs/component_contracts.md`
- Custom component example: `crates/pil/examples/custom_components.rs`
- Custom selector example: `crates/pil/examples/custom_selector.rs`
- Custom matcher example: `crates/pil/examples/custom_matcher.rs`
- Custom detector example: `crates/pil/examples/custom_detector.rs`
expression-atlas 0.1.0
Python client for searching and downloading gene expression datasets from EMBL-EBI Expression Atlas
Requires Python: >=3.9
Keywords: bioinformatics, ebi, expression-atlas, gene-expression, microarray, rna-seq

# Expression Atlas Python Client

Python client for searching and downloading gene expression datasets from [EMBL-EBI Expression Atlas](https://www.ebi.ac.uk/gxa), mirroring the R Bioconductor package.

## Features

- Search for Expression Atlas experiments by properties and species
- Download RNA-seq and microarray experiment data
- R-compatible data structures: `SummarizedExperiment` (RNA-seq) and `ExpressionSet` (microarray) wrapped in `SimpleList`
- Sync API with full type hints

## Installation

```bash
pip install expression-atlas
```

For development:

```bash
pip install -e ".[dev]"
```

## Quick Start

```python
from expression_atlas import ExpressionAtlasClient

client = ExpressionAtlasClient()

# Search experiments (DataFrame with Accession/Species/Type/Title)
results = client.search_experiments(properties=["cancer"], species="homo sapiens")

# Download a single experiment (SimpleList)
exp = client.get_experiment("E-MTAB-1624")

# RNA-seq example
rnaseq = exp["rnaseq"]  # SummarizedExperiment
counts = rnaseq.assays["counts"]  # numpy array genes × samples
sample_annotations = rnaseq.colData

# Microarray example
eset = exp["A-AFFY-126"]  # ExpressionSet
exprs = eset.exprs  # probes × samples
pheno = eset.phenoData

# Multiple experiments
exps = client.get_experiments(["E-MTAB-1624", "E-MTAB-1625"])
```

## Data Structures

### RNA-seq Data
RNA-seq experiments are returned as `SummarizedExperiment` objects containing:
- `assays["counts"]`: genes × samples matrix (orientation matches R package)
- `colData`: sample annotations
- `rowData`: gene annotations

### Microarray Data
Microarray experiments are returned as `ExpressionSet` objects containing:
- `exprs`: probes × samples matrix (orientation matches R package)
- `phenoData`: sample annotations
- `featureData`: probe annotations

## API Reference

### `ExpressionAtlasClient`

#### `search_experiments(properties, species=None)`
Search for experiments matching given properties.

**Parameters:**
- `properties`: List of search terms (e.g., `["cancer", "breast"]`)
- `species`: Optional species filter (e.g., `"homo sapiens"`)

**Returns:** `pandas.DataFrame` with columns: Accession, Species, Type, Title

#### `get_experiment(accession)`
Download a single experiment.

**Parameters:**
- `accession`: ArrayExpress/BioStudies accession (e.g., `"E-MTAB-1624"`)

**Returns:** `ExperimentSummary` object

#### `get_experiments(accessions)`
Download multiple experiments.

**Parameters:**
- `accessions`: List of accessions

**Returns:** Dictionary mapping accessions to `ExperimentSummary` objects

## License

GPL-3.0-or-later

## Links

- [Expression Atlas](https://www.ebi.ac.uk/gxa)
- [BioStudies](https://www.ebi.ac.uk/biostudies)
- [Contact Support](https://www.ebi.ac.uk/about/contact/support/gxa)