semvec/pypi-packages huggingface dataset:
dl-data-pipeline 0.1.1
Easy pipeline to process datas.
Requires Python: >=3.10

# dl_data_pipeline

`dl_data_pipeline` is a flexible and dynamic Python package for building data processing pipelines. It allows you to create pipelines using input nodes, chain them through various processing functions, and validate the outputs at different stages. The package supports deferred execution for dynamic graph building.

## Installation

You can install `dl_data_pipeline` via `pip`:

```bash
pip install dl_data_pipeline
```

Or, you can install it from the source:

```bash
git clone https://github.com/sacha-renault/DataPipeline.git
cd DataPipeline
pip install -r requirements_dist.txt
pip install python3 setup.py sdist bdist_wheel
```

## Basic example

Here’s a quick example to demonstrate how to set up and run a basic pipeline:

```python
# Import the necessary module
import dl_data_pipeline as dp
from dl_data_pipeline.process_functions import process_2d

# Define the inputs for the pipeline
input_node1 = dp.InputNode(name="1")

# Pass the input through functions to create the graph
x = process_2d.open_rgb_image(input_node1)
out1 = process_2d.padding_2d(x, (256,256), fill_value = 0.0)

# Create the pipeline by specifying the inputs and outputs
pipe = dp.Pipeline(inputs=[input_node1], outputs=[out1])

# Call the pipeline with the required inputs and get the outputs
img = pipe("path/to/image.png")
```

## documentation

For more advanced usage, including adding multiple inputs/outputs, custom validators, and deferred execution, please check the full documentation [here](https://sacha-renault.github.io/DataPipeline/).

## Contributing

We welcome anyone interested in adding their own preprocess functions to the package! If you have an idea or useful transformation you'd like to share, feel free to contribute by submitting a pull request. Your contributions are always appreciated!
pygsfit-cp 0.1.4
A python wrpper of the core fitting functionalities of gsFit.
Requires Python: >=3.8

# pygsfit_cp

`pygsfit_cp` is a radio imaging spectroscopy package dedicated to performing spectrum fitting. 
### INSTALLATION:
### New conda/virtual env (highly recommanded) please replace myenv with a desired name
`conda create --name myenv`
`conda activate myenv`
`pip install pygsfit_cp`

### USAGE:
Step1: There are two ways to do step 1. 
## Method One (Recommended)

a. **Initial Setup and Exploration**
   - Run `/demo/pygsfit_cp.ipynb` to identify appropriate initial gas parameters and fitting ranges for each parameter.
   - Define and visualize the Region of Interest (ROI).

b. **Export Batch Script**
   - After obtaining the ideal fitting results, export a batch mode fitting script to perform the fitting with the same parameters.

## Method Two

a. **Direct Batch Mode**
   - If you are already familiar with your data, directly run `/demo/batch_mode_script.py`.
   - Manually input all the parameters of the ROI.

## Common Steps for Both Methods

2. **Execute Batch Script**
   - Execute the batch script to obtain the fitting results, which include:
     - A results file (`fitting_results.h5`)
     - An IDL script (`make_params_maps.pro`)
     - Several intermediate files.

3. **Generate Parameter Maps**
   - Run the IDL script (`make_params_maps.pro`) directly in `sswidl` to generate an IDL save file (`parameters_map.sav`).
   - The resulting save file can be opened in `gsfitview`, `sswidl`.
xarray-graph 2024.9.1
PyQt/PySide UI for graphing (x,y) slices of Xarray datasets.
Requires Python: >=3.10
Keywords: PyQt, PySide, xarray, graph

# xarray-graph
PyQt/PySide UI for graphing (x,y) slices of Xarray Datasets in a DataTree.

![GitHub Tag](https://img.shields.io/github/v/tag/marcel-goldschen-ohm/xarray-graph?cacheSeconds=1)
![build-test](https://github.com/marcel-goldschen-ohm/xarray-graph/actions/workflows/build-test.yml/badge.svg)
![GitHub Release](https://img.shields.io/github/v/release/marcel-goldschen-ohm/xarray-graph?include_prereleases&cacheSeconds=1)
![publish](https://github.com/marcel-goldschen-ohm/xarray-graph/actions/workflows/publish.yml/badge.svg)

- [Install](#install)
- [Run](#run)
- [Documentation](#documentation)

## Install
Requires a PyQt package. Should work with PySide6, PyQt6, or PyQt5.
```shell
pip install PySide6
```
Install latest release version:
```shell
pip install xarray-graph
```
Or install latest development version:
```shell
pip install xarray-graph@git+https://github.com/marcel-goldschen-ohm/xarray-graph
```

## Run
Launch the GUI:
```shell
xarray-graph
```

## Documentation
:construction: