semvec/pypi-packages huggingface dataset:
SpaMeanImpute 0.1.0
Spatial Transcriptomics Imputation Tool (SpaMean-Impute)
Requires Python: >=3.7

# SpaMeanImpute

**Spatial Transcriptomics Imputation Tool (SpaMean-Impute)**

SpaMeanImpute is a Python package for imputing missing gene expression values in spatial transcriptomics datasets.  
It leverages spatial neighborhood information to improve downstream clustering and analysis.

---

## ✨ Features

- **Spatially-aware imputation** using k-nearest neighbors in spatial coordinates.
- **Highly variable gene selection** with `scanpy`.
- **Evaluation pipeline** for imputation and clustering performance.
- **Command-line interface (CLI)** for easy integration into workflows.
- **Compatible with `.h5ad` AnnData files**.

---

## 📦 Installation

You can install SpaMeanImpute from PyPI:

```bash
pip install SpaMeanImpute
```
Or install directly from the GitHub repository:
```bash
git clone https://github.com/FahimHafiz/SpaMean-Impute/tree/main/SpaMeanImpute
cd SpaMeanImpute
pip install -e .
```

## 🚀 Usage

After installation, you can run:
```bash
spamean-impute input_file.h5ad \
    -k 9 \
    --threshold 0.1 \
    --n_top 5000 \
    --output_file imputed_output.h5ad
```

Arguments:

input_file — Path to input .h5ad file.

-k — Number of spatial neighbors (default: 9).

--threshold — Drop threshold for zero imputation (default: 0.1).

--n_top — Number of highly variable genes (default: 5000, use 'all' to skip filtering).

--output_file — Path to save the imputed .h5ad file.


## 🐍 Python API

```python
import spa_mean_impute
from spa_mean_impute.imputer import SpaMeanImpute

imputer = SpaMeanImpute(k=9, threshold=0.1, n_top=5000)
results = imputer.run("input_file.h5ad", output_file="imputed_output.h5ad")

print("Results:", results)
```


## 📁 Project Structure

spa_mean_impute/
    __init__.py
    imputer.py
    cli.py
    utils.py
tests/
    test_imputer.py
setup.py
pyproject.toml
README.md
LICENSE.txt


## ✅ Running Tests

python -m unittest discover tests


## 📄 License

This project is licensed under the MIT License — see the LICENSE.txt file for details. For any problem, feel free to contact [fahimhafiz@cse.uiu.ac.bd](fahimhafiz@cse.uiu.ac.bd) or create an issue!
hyram 6.1
Hydrogen Plus Other Alternative Fuels Risk Assessment Models Python Package
Requires Python: >=3.9
Keywords: hydrogen, methane, propane, flame, plume, jet, overpressure, quantitative risk assessment, risk

# HyRAM+ Python Package

The Hydrogen Plus Other Alternative Fuels Risk Assessment Models (HyRAM+) Python package contains modules for the calculation of physical release and risk for alternative fuels.


## Installation

To install [HyRAM+ from the Python Package Index](https://pypi.org/project/hyram/), use the command:

```
pip install hyram
```

More instructions on installing Python packages with `pip` can be found [here](https://packaging.python.org/en/latest/tutorials/installing-packages/).

To install [HyRAM+ from Conda-Forge](https://anaconda.org/conda-forge/hyram), use the command:

```
conda install -c conda-forge hyram
```

More instructions on installing Python packages with `conda` can be found [here](https://docs.anaconda.com/anacondaorg/user-guide/howto/#use-packages).


## Usage

The HyRAM+ package can be utilized after installation using a standard Python import:
```python
import hyram
```

Specific models can be accessed within the HyRAM+ package.
The main calculations for the physics models can be accessed using `hyram.phys.api`.
This includes models such as:
* `create_fluid`
* `compute_mass_flow`
* `compute_tank_mass_param`
* `compute_thermo_param`
* `compute_equivalent_tnt_mass`
* `analyze_jet_plume`
* `analyze_accumulation`
* `jet_flame_analysis`
* `compute_overpressure`

The following are the main model objects that can be accessed using `hyram.phys`:
* `Fluid`
* `Orifice`
* `Jet`
* `Flame`
* `Source`
* `Enclosure`
* `Vent`
* `IndoorRelease`
* `BST_method`
* `TNT_method`
* `Bauwens_method`
* `FuelProperties`

The main risk calculations are located in `hyram.qra.analysis.conduct_analysis`.

More information about the specific models and the inputs/outputs for each one can be found in the specific modules themselves.


## Documentation

The models and equations used in the HyRAM+ Python package are described in the Technical Reference Manual, located on the [HyRAM+ website](https://hyram.sandia.gov).


## Development/Modifications

To make changes to the source code, either for a specific need or to contribute to HyRAM+, please follow the instructions in the [CONTRIBUTING](https://github.com/sandialabs/hyram/blob/master/CONTRIBUTING.md) file for the HyRAM+ Python package.
django-lti-dynamic-registration 0.1.2
Add-on to django-lti to support dynamic registration.
Requires Python: >=3.10

# django-lti-dynamic-registration

Add-on to django-lti to support dynamic registration.

See: https://www.imsglobal.org/spec/lti-dr/v1p0

More details to come.

## Using

```python
from lti_dynamic_registration.views import DynamicRegistrationBaseView
from lti_dynamic_registration.types import (
    CanvasLtiMessage,
    CanvasLtiRegistration,
    CanvasLtiToolConfiguration,
)
from django.shortcuts import redirect, render


class DynamicRegistrationView(DynamicRegistrationBaseView):
    tool_friendly_name = "Tool Name"

    def get(self, request, *args, **kwargs) -> HttpResponse:
        # Return a form to the user where they can customize the registration
        return render(request, 'registration.html')


    def post(self, request, *args, **kwargs) -> HttpResponse:
        # Perform the registration steps. Typically this would involve:
        # 1. Register the platform in the tool
        # 2. Register the tool in the platform
        # 3. Update the platform registration with the client ID returned in step 2


        # Return a page containing javascript that calls a special
        # platform postMessage endpoint
        return self.success_response()
```