semvec/pypi-packages huggingface dataset:
starstack 0.2.14
A relion star mrcs stack class
Keywords: relion cryoem star stack mrcs

## starstack: CryoEM Particle stars and stack Management in Python


# Overview
The ParticlesStarSet class provides an efficient way to manage a set of particles in cryo-electron microscopy (cryoEM). 
It allows for easy reading and manipulation of STAR files and associated .mrcs image stacks. 
The library also offers utilities for creating subsets and saving them back to disk.

# Features

Read STAR files and associated .mrcs particle stacks.
Create new STAR files and .mrcs particle stacks.
Support for creating subsets of particles.
Support for particle stack file handling.

# Dependencies
mrcfile
numpy
pandas
starfile

# Installation
pip install git+https://github.com/rsanchezgarc/starstack

# Usage
Reading STAR Files

```
from starstack import ParticlesStarSet
pset = ParticlesStarSet("my_file.star", particlesDir="Particles/")
for img,metadata in pset:
  print(img.shape)
```
aiida-nanotech-empa 1.0.0b12
AiiDA plugins and workflows developed at nanotech@surfaces group from Empa.
Requires Python: >=3.9

[![Build Status](https://github.com/nanotech-empa/aiida-nanotech-empa/workflows/ci/badge.svg?branch=master)](https://github.com/nanotech-empa/aiida-nanotech-empa/actions)
[![codecov](https://codecov.io/gh/nanotech-empa/aiida-nanotech-empa/branch/develop/graph/badge.svg?token=52ACMY55UQ)](https://codecov.io/gh/nanotech-empa/aiida-nanotech-empa)
[![PyPI version](https://badge.fury.io/py/aiida-nanotech-empa.svg)](https://badge.fury.io/py/aiida-nanotech-empa)
[![DOI](https://zenodo.org/badge/275159349.svg)](https://zenodo.org/badge/latestdoi/275159349)

# aiida-nanotech-empa

AiiDA library containing plugins/workflows developed at nanotech@surfaces group from Empa.

Contents:

* `nanotech_empa.nanoribbon`: work chain to characterize 1D periodic systems based on Quantum Espresso.

* `nanotech_empa.gaussian.spin`: Work chain to characterize spin properties of molecular systems with Gaussian. Calls multiple child work chains. Steps:
  * Wavefunction stability is tested for each spin multiplicity
  * Geometry is relaxed for the different spin states and ground state is found
  * Property calcuation on the ground state: ionization potential and electron affinity with Δ-SCF, natural orbital analysis in case of open-shell singlet
  * Vertical excitation energies for non-ground state multiplicities
  * Orbitals and densities are rendered with PyMOL (needs to be installed separately as a python library, e.g. from [pymol-open-source](https://github.com/schrodinger/pymol-open-source/tree/v2.4.0))

## Installation

```shell
pip install aiida-nanotech-empa
```

## For maintainers

To create a new release, clone the repository, install development dependencies with `pip install '.[dev]'`, and then execute `bumpver update --dry --major (--minor/--patch)`.
This will display the changes that will be made to the repository - check them carefully.

Once you are happy with the changes, remove the `--dry` option and re-execute the command.
This will:

  1. Create a tagged release with bumped version and push it to the repository.
  2. Trigger a GitHub actions workflow that creates a GitHub release.



Additional notes:

  - The release tag (e.g. a/b/rc) is determined from the last release.
    Use the `--tag beta (alpha/gamma)`  option to switch the release tag.

## Acknowledgements
We acknowledge support from:
* the [NCCR MARVEL](http://nccr-marvel.ch/) funded by the Swiss National Science Foundation;
* the [swissuniversities P-5 project "Materials Cloud"](https://www.materialscloud.org/swissuniversities).
<img src="https://github.com/nanotech-empa/aiida-nanotech-empa/blob/master/images/MARVEL.png" width="250px" height="131px"/>
<img src="https://github.com/nanotech-empa/aiida-nanotech-empa/blob/master/images/swissuniversities.png" width="300px" height="35px"/>
Google-Analytic-Process 0.2.0
A simplified Python client for making raw Google Analytics 4 API calls
Requires Python: >=3.8

# Google Analytics Process

A Python library for processing Google Analytics 4 data with built-in data transformations and channel grouping logic.

## Installation

```bash
pip install Google-Analytic-Process
```

## Quick Start

```python
from ga4_analytics import GA4Processor
from datetime import datetime, timedelta

# Initialize processor
processor = GA4Processor(
    credentials_path="/path/to/your/credentials.json",
    property_id="your_ga4_property_id"
)

# Define date range
end_date = datetime.now().date()
start_date = end_date - timedelta(days=7)

# Fetch and process general report with default dimensions/metrics
df = processor.get_general_report(start_date, end_date)

# Or specify custom dimensions and metrics
custom_dimensions = ["date", "sessionDefaultChannelGroup", "deviceCategory"]
custom_metrics = ["sessions", "totalRevenue", "transactions"]
df_custom = processor.get_general_report(
    start_date, end_date,
    dimensions=custom_dimensions,
    metrics=custom_metrics
)

# For completely custom reports with optional processing
df_raw = processor.get_custom_report(
    start_date, end_date,
    dimensions=custom_dimensions,
    metrics=custom_metrics,
    apply_processing=False  # Get raw data without transformations
)

print(df.head())
```

## Features

- Easy GA4 API integration
- **Flexible dimensions and metrics** - specify your own or use defaults
- **Custom report generation** - with optional data processing
- Built-in data transformations and cleaning
- Channel grouping and campaign labeling
- Revenue reallocation logic
- Email campaign data integration
- Configurable data formatting

## Features in v0.1.0

- ✅ **Custom dimensions and metrics** in `get_general_report()`
- ✅ **New `get_custom_report()` method** for full flexibility
- ✅ **Optional data processing** - get raw data or processed data
- ✅ **Easy GA4 API integration**

## Requirements

- Python 3.8+
- Google Analytics Data API credentials
- pandas, numpy, google-analytics-data

## License

MIT License