semvec/pypi-packages huggingface dataset:
lvm-read-comma 1.32
LabView Measurement File Reader
Requires Python: >=3.8

lvm_read
========

LabView Measurement file reader
-------------------------------

|pytest|

|binder| to test the *Showcase lvm_read.ipynb*. 

.. |binder| image:: http://mybinder.org/badge.svg 
   :target: http://mybinder.org:/repo/ladisk/lvm_read
.. |pytest| image:: https://github.com/ladisk/lvm_read/actions/workflows/python-package.yml/badge.svg
    :target: https://github.com/ladisk/lvm_read/actions

See also `Specifications <http://www.ni.com/tutorial/4139/en/>`_.
MOBiceps 0.1.4
Python tools for Mass Spectrometry and Omics data.
Requires Python: ==3.8.*

# MOBiceps
![MOBicpes logo](./images/MOBiceps.png)
MOBiceps is a collection of python functions for omics and mass spectrometry data. It is the working arm of [MOAgent](https://github.com/wollscheidlab/MOAgent). An early version of its core function featureSelector.py was first time applied in the work [Nature Communications, 2023](https://www.nature.com/articles/s41467-023-42101-z) to identify phenotype-specific proteins in myeloproliferative neoplasms (blood cancer).
If you have any questions please do not hesitate to contact jsettelmeier@ethz.ch

## Basic instructions

You can use MOBiceps by cloning the repo to your local machine using

```bash
$ git clone https://github.com/wollscheidlab/MOBiceps.git
```
or using the python package distribution system pip via

```bash
$ pip install MOBiceps
```

## Notes
Developed and tested under python 3.8.
libertem-blobfinder 0.6.1
LiberTEM correlation and refinement library
Requires Python: >=3.9
Keywords: electron microscopy

.. _`strain mapping`:

LiberTEM-blobfinder is a package for correlation-based peak finding and strain
mapping. It is part of the `LiberTEM project
<https://libertem.github.io/LiberTEM/>`_.

Usage
-----

See `Usage documentation <https://libertem.github.io/LiberTEM-blobfinder/usage.html>`_!


Installation
------------

The quick version from PyPI:

.. code-block:: shell

    $ pip install libertem-blobfinder[udf]

See `full installation instructions
<https://libertem.github.io/LiberTEM-blobfinder/install.html>`_ for more
details!

.. rubric:: Reference

See `blobfinder API reference
<https://libertem.github.io/LiberTEM-blobfinder/reference.html>`_ for details!

.. rubric:: Examples

See `example applications
<https://libertem.github.io/LiberTEM-blobfinder/examples.html>`_!

License
-------

LiberTEM-blobfinder is licensed under GPLv3.
web_skeleton3 2.1.3
Static html code generator.
Requires Python: >=3.8

web-skeleton3
=============

Generator of static html/css/js code tree.

Based on:
    - mako_
    - webassets_

.. _mako: https://pypi.python.org/pypi/Mako
.. _webassets: https://pypi.python.org/pypi/webassets

Install
-------

Install with::

    pip3 install web-skeleton3

Remember install::

    sudo apt-get install ruby-dev ruby-rubygems
    sudo gem install compass
    sudo gem install sass
    npm install clean-css-cli -g
    npm install uglify-js -g
    sudo pip3 install scour
    # sudo pip3 install cssutils
    #sudo pip3 install rjsmin

Upload to pypi
--------------

flit build
flit publish

License
-------

Licensed under the  `The MIT License <http://www.opensource.org/licenses/mit-license>`_.
See LICENSE.txt in the source distribution for details.
spc-toolbox 0.1.2
A toolbox for Statistical Process Control
Requires Python: <4.0,>=3.10


# Statistical Process Control Toolbox Documentation

Welcome to the Statistical Process Control (SPC) Toolbox Documentation. This toolbox provides a comprehensive set of tools for monitoring and analyzing manufacturing processes. The toolbox is designed to help quality assurance professionals and engineers to maintain high standards of quality by identifying variations in processes using various SPC charts.

## Installation

To install the SPC Toolbox, use the following command:

```bash
pip install spc-toolbox
```

## Quick Start

Here's a quick example of how to use the XBarChart class to monitor process means:

```python
import pandas as pd
from spc_toolbox import XBarChart

# Load data from CSV
df = pd.read_csv("data/XBar-RChart.csv")

# Initialize XBarChart
xbarChart = XBarChart()

# Fit data to the chart
xbarChart.fit(index=df.index, values=df, axis=1, dispersion_type="range")

# Plot the chart
xbarChart.plot()
```

This example demonstrates how to create an `XBarChart` object, fit it with sample data, and plot the control chart to visualize the process means.

##  Control Charts

The SPC Toolbox provides several types of control charts, each designed to monitor different aspects of a manufacturing process. Here are the main control charts available in the toolbox:

- [XBarChart](docs/XBarChart.md): Monitors the average of subgroup means in a process.
- [RChart](docs/RChart.md): Tracks the range of values within subgroups to assess process variability.
- [SChart](docs/SChart.md): Monitors the standard deviation of subgroups to evaluate process dispersion.
- [UChart](docs/UChart.md): Tracks the number of defects per unit of output to assess process quality.
- [PChart](docs/PChart.md): Monitors the proportion of defective items in a sample to evaluate process quality.
- [IChart](docs/IChart.md): Tracks the number of defects per unit of output in a Poisson process.
- [MRChart](docs/MRChart.md): Monitors the moving range of consecutive observations to assess process variability.

Each control chart class provides methods for fitting data, plotting the control chart, and accessing statistical parameters.

## Contributing

If you would like to contribute to the SPC Toolbox, please read our [contributing guidelines](CONTRIBUTING.md) for more information on how to get started.

## License

The SPC Toolbox is released under the [MIT License](LICENSE).