semvec/pypi-packages huggingface dataset:
perturbopy 0.7.2
Suite of Python scripts for Perturbo testing and postprocessing
Requires Python: >=3.7.12

# perturbopy - Python suite for Perturbo postprocessing and testing

This package provides utilities to postprocess and test the Perturbo code, which is an open-source software to compute from first principles the scattering processes between charge carriers (electrons and holes) and phonons, defects, and photons in solid state materials, including metals, semiconductors, oxides, and insulators. Perturbo is written in modern Fortran, including efficient MPI and OpenMP parallelization, allowing to perform calculations efficient on supercomputers using up to ~100,000 cores. Read more about the main code Perturbo [here](https://perturbo-code.github.io/index.html).

Perturbopy provides a user-friendly object-oriented environment to postprocess Perturbo calculations, such as band structure interpolation, electron transport, ultrafast carrier dynamics, and many more. Using Perturbopy, a user can easily load the Perturbo output files (HDF5 or YAML) into Python objects, perform calculations, and visualize data.

The second purpose of the Perturbopy package is to test the main Perturbo code. After a compilation of the Perturbo code on a new machine or after a new implementation, it is always a good practice to run the testsuite, verifying that the existing functionality was not affected, and add new test cases to ensure that the newly implemented features will be compatible with future implementations. Perturbopy, using the `pytest` package, provides extensive testing of Perturbo, allowing for both serial and parallel runs.

Find more about Perturbopy on its [documentation website](https://perturbopy.readthedocs.io). We are open to collaboration! Please create a fork and submit a pull request with detailed description of your work, and we will be happy to discuss and include your contribution.
ts-storagetools 0.2.2
SQLite schema, Python classes and data management tools for working with time series from engineering test beds
Requires Python: >=3.13

# Time Series Storage Tools

This software can be used to store time series from manufacturing test beds. 
The design was derived with data analysis in mind, enabling FAIR principles and reproducibility.

The conceptualization is published in [*link will follow shortly*].
If you use this software in any of your works please cite [*DOI will follow shortly*].


# Setup storagetools

To use `storagetools` in your projects, you need the following:

-   `storagetools` installed
-   a `config.ini` (works without but is not useful, see [Getting Started](#getting-started))

A minimal `config.ini` may look like:

```
[environment]
analysis_name = my_first_analysis 

[property_columns]
# additional case-specific properties (metadata information) as dict of lists.
# Use SQLite datatype as key for the dict (e.g., "TEXT", "FLOAT", etc.)
experiment = {"INT": ["run", "material"], "FLOAT": ["feedrate"]}
sensor = {"TEXT": ["manufacturer", "model", "identifier", "description"]}
channel = {"TEXT": ["measurand", "unit"]}
```


A more complete `config.ini` may look like:

```
[environment]
analysis_name = ucb_mill
available_cores = 12

# upper size limit for the key-value store, for most cases just leave as is
max_kvsize = 1099511627776

[storagetools.locations]
# name of the working directory
workdir_root = work

# name of the data directory within the work directory
storage_dir = data

# name of the plots directory within the work directory
plot_dir = plots

# name of the SQLite database file
database_filename = database.db

[property_columns]
# additional case-specific properties (metadata information) as dict of lists.
# Use SQLite datatype as key for the dict (e.g., "TEXT", "FLOAT", etc.)
experiment = {"INT": ["run", "material"], "FLOAT": ["feedrate"]}
sensor = {"TEXT": ["manufacturer", "model", "identifier", "description"]}
channel = {"TEXT": ["measurand", "unit"]}
```

If you want to modify logging, you can add:

```
[loggers]
keys=root

[handlers]
keys=screen, file

[formatters]
keys=default

[logger_root]
level=DEBUG
handlers=screen,file

[handler_screen]
class=StreamHandler
level=INFO
formatter=default
args=(sys.stdout,)

[handler_file]
class=FileHandler
level=DEBUG
formatter=default
args=('debug.log', 'a')

[formatter_default]
format=%(asctime)s %(levelname)s %(filename)s:%(lineno)d (%(funcName)s): %(message)s
```


# Getting Started

For a demonstration of how to use storagetools refer to the jupyter-notebooks in `doc/tutorials`.