semvec/pypi-packages huggingface dataset:
statit-py 1.0.0b2
Utilities for gostatit.com
Requires Python: >=3.7

# statit_py
A package to access the [Statit](https://gostatit.com) API

## Installation

To install the current release:

```
$ pip install statit_py
```

## Quick usage

To use the Statit REST API methods, first authenthicate yourself with your username and API token (see [Signing-in](https://help.gostatit.com/excel/signin/#authentication)):

```py
import statit_py

statitAPI = statit_py.coreAPI('<username>', '<api_key>')
```

`GET`, `LIST` and `DELETE` actions for collections and series are available in reference-by-ID format:

```py
data = statitAPI.getSerie('<serie_id>')
```

Alongside with the remaining `PUT` and all their respective `batch`  actions, they are also available in standard JSON request format:

```py
statitAPI.putSerieJSON({
    'id': '<serie_id>',
    ...
})
```

For the full list of methods and their detailed arguments, see the [Python Documentation](https://help.gostatit.com/dg/py/).
pitchtrack 0.0.1
collection of pitch (f0, fundamental frequency) tracking algorithms with unified interface
Requires Python: >=3.10

# pitchtrack
collection of pitch (f0, fundamental frequency) tracking algorithms with unified interface

warning: currently this repo is only contains mock API. Actual implementation is here https://github.com/tandav/pitch-detectors (to be merged)

# Installation

```shell
pip install pitchtrack
```

# Usage

```python
import pitchtrack
pitchtrack.test()
```
sICTA 1.0.2
A marker-based cell type annotation method that combines the self-training strategy with pseudo-labeling and the nonlinear association capturing capability of Transformer.
Requires Python: >=3.8

# sICTA: Interpretable Cell Type Annotation based on self-training

The full description of sICTA and its application on published single cell RNA-seq datasets are available.

Download archive with preprocessed data at: https://drive.google.com/drive/folders/1jbqSxacL_IDIZ4uPjq220C9Kv024m9eL.

The repository includes detailed installation instructions and requirements, scripts and demos.


## 1 The workflow of sICTA.

![](https://github.com/nbnbhwyy/sICTA/raw/main/Flow.jpg)

**(a)** Combining cell expression and marker gene specificity to generate pseudo-labels. **(b)**  The downstream Transformer classifiers are first pre-trained based on cell type probability distributions (pseudo-labels), followed by iterative refinement of the classifiers through a self-training framework until convergence. The sICTA takes the a priori knowledge from the biological domain and uses masked learnable embeddings to transform the input data ($G$ genes) into $k$ input tokens representing each gene set (GS) and a class token (CLS).
## 2 Requirements

+ Linux/UNIX/Windows system
+ Python == 3.8.6
+ torch == 1.12.1
+ scanpy == 1.9.1

Topic_gene_embedding

## 3 Usage

### Data format

sICTA requires cell-by-cell-gene matrix and cell type information to be entered in csv object format.
We provide default data for users to understand and debug sICTA code.

### Installation and implementation

**Installation via github:**

Download sICTA via github clone, you can run it directly by main.py file.
```bash
python main.py
```

**Installation via PyPI:**

After installing and importing sICTA via PyPI, a notebook tutorial can be found at tutorial.ipynb.
```bash
python -m venv sICTA-env
source sICTA-env/bin/activate 
pip install sICTA
```

## Reference

If you use `sICTA` in your work, please cite