semvec/pypi-packages huggingface dataset:
climate-finance 1.2.2
Climate Finance data
Requires Python: >=3.11
Keywords: climate, finance, data, oecd, unfccc

[![pypi](https://img.shields.io/pypi/v/climate_finance.svg)](https://pypi.org/project/climate_finance/)
[![python](https://img.shields.io/pypi/pyversions/climate_finance.svg)](https://pypi.org/project/climate_finance/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

# The climate finance package

_**climate-finance** is the python package to get, clean, and work with international public climate finance._ 

You can use this package to get, rebuild, remix, and create using our tools and methodologies — all with only a few lines of code.

Climate finance data is notoriously difficult to work with. It's messy - really messy - and comes in all sorts of shapes and sizes, scattered across multiple websites.

It took us months to understand which climate finance data to use, and even longer to clean the data ready for the Climate Finance Files. 

We don't think you should have to do this too. 

We have built **climate-finance** to lower the barriers to access that many organisations face when seeking to conduct research or advocacy on these topics. For too long, bad data has restricted climate accoutability. 
And for too long, global leaders have capitalised on bad data to dictate the narrative on climate finance. 

We hope these tools equip everyone with the data to hold global leaders accountable in the fight against climate change. As currently, they are not doing enough. 

## Getting started 
This package provides a set of tools to help you work with climate finance data.
It can be used to:

- download data from the OECD databases (The Creditor Reporting System (CRS)
  and the Climate-related Development Finance database (CRDF))
- download data from the UNFCCC data portal
- clean and harmonise data from the different sources
- convert climate finance data to different currencies and prices

### Set up

The Climate Finance package is composed of many different tools to download, process
and analyse data.

To get started, you will need to install the package. You can do this from pipy using pip:

```bash

pip install climate_finance --upgrade

```

or directly from the source code:

```bash

pip install git+https://github.com/ONEcampaign/climate-finance-package.git

```

### Basic Usage

The first step when using the Climate Finance package should be to set a working directory
where the data will be stored. This can be done importing using the
`set_climate_finance_data_path` function:

```python
from climate_finance import set_climate_finance_data_path

set_climate_finance_data_path('path/to/your/data')

```

The easiest way to interact with the data is through the `ClimateData` class.

**For a detailed overview of how to use the ClimateData class,[ please see its documentation.](./climate_finance/README.md)**

## Questions? Would like to collaborate?
We want this package to help others analyse climate finance data. If you want to collaborate, or have any questions, please reach out.
epi2hit 0.1.2
Pipeline for integrative methylation / expression analysis and eCpG detection
Requires Python: >=3.8

# epi2hit

`epi2hit` is a Python package for **integrative methylation / expression analysis** and detection of **epigenetically deregulated CpGs (eCpGs)**

---

## Features
  
- Filtering of probes to **open chromatin** using ATAC-seq peaks  
- Mapping probes to genes  
- Preliminary QC / filtering of probes  
- Annotation with chromatin state (Pomerantz / chromHMM states)  
- Correlation-based probe selection and duplicate resolution  
- Annotation of probes with **3D chromatin loops**  
- Preparation of TCGA-like expression dictionaries  
- Linear regression between methylation and expression  
- Peak-based methylation categorisation and detection of eCpGs

All steps are available as individual functions, but they are designed to run in a simple end-to-end script or notebook.

---

## Installation

pip install epi2hit
x3cflux 3.0.1
Python interface for 13CFLUX
Requires Python: >=3.9
Keywords: 13C-MFA, MFA, mass-spectrometry, HPC

<img src="https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/-/raw/main/docs/source/images/banner.png" width=350>

[![Pipeline Status](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/badges/main/pipeline.svg)](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/-/pipelines?scope=all&page=1&ref=main)
[![Coverage Report](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/badges/main/coverage.svg)](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/-/commits/main)
[![Latest Release](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/-/badges/release.svg)](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX/-/releases)

is a high-performance simulator for isotopically stationary and non-stationary simulation of isotope labeling
experiments. It is build on highly optimized C++ code, made available in Python through `pybind11`. It comprises the
following main functions:

- Read metabolic models and labeling data from standardized FluxML
- Simulation of labeling distributions and data residuals given fluxes and metabolite concentrations
- Fitting and statistical analysis (Frequentist and Bayesian) of <sup>13</sup>C labeling experiments

``13CFLUX`` is being developed by the Modelling and Simulation group under the supervision of Katharina Nöh at the
IBG-1, Forschungszentrum Jülich, Germany.

## Installation

The recommended way for practitioners to install `13CFLUX` is via `pip` from the Python Package Index (PyPI). It is
recommended to do this in a Python environment. Before installation, you should make sure to have `ipopt` installed
(either [directly](https://coin-or.github.io/Ipopt/INSTALL.html) or by installing
[cyipopt via conda](https://cyipopt.readthedocs.io/en/stable/install.html)). Then just run

```shell
pip install x3cflux
```

## Documentation

We provide a [Read The Docs documentation](https://x3cflux.readthedocs.io). Check it out!

## Development

The development of *13CFLUX* primarily takes place on
[JuGit](https://jugit.fz-juelich.de/IBG-1/ModSim/Fluxomics/13CFLUX), where we have access to powerful continuous
integration and a Docker registry. **Please report issues and request features only JuGit.**

## Citation

```
@article{Stratmann2025,
    author = {Stratmann, Anton and Beyß, Martin and Jadebeck, Johann F and Wiechert, Wolfgang and Nöh, Katharina},
    title = {13CFLUX — Third-generation high-performance engine for isotopically (non)stationary 13C metabolic flux analysis},
    journal = {Bioinformatics},
    pages = {btaf630},
    year = {2025},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btaf630},
    url = {https://doi.org/10.1093/bioinformatics/btaf630},
}
```

## Further links

- PyPI: https://pypi.org/project/x3cflux/
- Github Mirror: https://github.com/JuBiotech/13CFLUX
tsim 0.1.0
A lightweight library for quantifying the similarity between two strings

<div align="center">
<h1>TSim</h1>

<img alt="GitHub Created At" src="https://img.shields.io/github/created-at/Sanyam-Asthana/TSim">
<img alt="GitHub commit activity" src="https://img.shields.io/github/commit-activity/t/Sanyam-Asthana/TSim">
<img alt="GitHub contributors" src="https://img.shields.io/github/contributors/Sanyam-Asthana/TSim">
<br>
<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/Sanyam-Asthana/TSim">
<img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/tsimt?label=PyPI%20downloads">

<p>A lightweight string similarity quantifier, with support for names</p>
</div>

## What it does
TSim (Text Similarity) is a quantifier for similarity of strings. It reports the extent of similarity of two strings. TSim accounts for errors like OCR errors, different ordering, abbreviation and more.

## Features

- Compensation for OCR errors related to similar looking characters (eg. 5 and S; 0 and O)
- Compensation for over-detection and under-detection of text by OCR systems
- Compensation for different ordering of names (eg. FirstName-LastName and LastName-FirstName)
- Compensation for abbreviation in names (eg. Sanyam Asthana and S. Asthana)

## Installation
TSim can be installed via pip:

```bash
pip install tsim
```
You can verify the installation by running:

```bash
pip show tsim
```

## Usage
To use TSim, you first need to install it using pip.

To use TSim in a project, you need to import the library in your project using `import tsim`

## Functions
### get_confidence()
Returns the confidence/error of similarity of two strings

**Parameters:**<br>
`received_str` – The string to compare<br>
`expected_str` – The string to be expected in the comparison<br>
`mode` – `"c"` to return confidence, `"e"` to return the error<br>

**Returns:** Confidence (0-1)/Error depending on the mode used

### get_abbreviated_confidence()
Returns the confidence of similarity of two strings taking into account abbreviated part of the string

**Parameters:**<br>
`received_str` – The string to compare<br>
`expected_str` – The string to be expected in the comparison<br>
`mode` – `"c"` to return confidence, `"e"` to return the error<br>

**Returns:** Confidence (0-1)/Error depending on the mode used

### get_name_confidence()
Returns the confidence of similarity between two names

**Parameters:**<br>
`received_name` – The name to compare<br>
`expected_name` – The name to be expected in the comparison<br>

**Returns:** The confidence level of the similarity of the names (0-1)

## Technical Details

- Python 3.7+
- Uses an error based confidence system, with different penalties per kind of error.
- Uses a dictionary for OCR character similarity matching

## License
MIT License

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