semvec/pypi-packages huggingface dataset:
m2py 0.0.4
Materials Morphology Python Package
Requires Python: >=3.6

# m2py: Materials Morphology Python Package

## Contributors

- Wesley Tatum, PhD Student at University of Washington MSE Department
- Diego Torrejon, Staff Machine Learning Engineer at BlackSky
- Patrick O’Neil, Director of Machine Learning and Artificial Intelligence at BlackSky

## Requirements

You need Python 3.6 or later to work with m2py.

## Installation

### Through github:
`Clone or download this repository`

`pip install -r requirements.txt`

### Through pip:
`pip install m2py`

## Usage
At the moment, we are offering two introductory tutorials found under the `tutorials` directory. There is a basic tutorial showing the main processing capabilities of m2py. There's also an advanced tutorial showing how users can use current m2py capabilities to make them suitable for their own applications.

## Background

SPM techniques have been pivotal in understanding surfaces, morphologies, and intermolecular interactions of matter from the atomic to millimeter scales. Probes interacting with the material surface produce 2-dimensional images of the topography with intensity scales representing any number of material properties (e.g. modulus, conductivity, or capacitance). In many experiments, multiple properties can be imaged simultaneously, producing a 3-dimensional stack of these images.

By utilizing different combinations of computer vision and machine learning techniques, m2py leverages differences in imaged material properties to recognize different material phases, as well as different domains and topographical features. First, outlier pixels or signals can be removed. Next, signals are compressed to only the most informative features via PCA. These signals can be deconvoluted via GMM, or some other semantic segmenter for phase labeling. Finally, an instance segmenter, such as Persistence Watershed Segmentation, clusters the phase-labeled pixels into domains, which receive another label.

Once labeled by m2py segmenters, quantitative descriptions of each domain and the total morphology are extracted and can be used to train supervised models to predict material properties or performance. Such supervised training has not been accessible to most SPM researchers, due to the labor-intensive nature of manual-labeling.
exheader 0.0.1
Library for handling 3ds exheader files

# exheader

This is a simple python library allowing to load, modify and save 3ds exheader files.
Not all settings are supported (those will throw an exception on load) or easily modifyable yet.

## Installation

Run the following to install:

```python
pip install exheader
```

## Usage

```python
from exheader import Exheader

exh = Exheader()

# Load exheader from file
f_in = open('exheader.bin', 'rb')
exh.unpack_from(f_in.read())
f_in.close()

# Print the application tite
print(exh.sci.app_title)

# Change the application title
exh.sci.app_title = 'MyApp'

# Print allowed SVCs
print(exh.aci.arm11_kernel_caps.svcs)

# Add SVC 0x70
exh.aci.arm11_kernel_caps.svcs.add(0x70)

# Write modified exheader to file
f_out = open('exheader_out.bin', 'wb')
f_out.write(exh.pack())
f_out.close()
```
pyComBat-test 0.1.2
pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods
Requires Python: >=3.6

# pyComBat

pyComBat [1] is a Python 3 implementation of ComBat [2], one of the most widely used tool for correcting technical biases, called batch effects, in microarray expression data.

More detailed documentation can be found at [this address](https://epigenelabs.github.io/pyComBat/).

## Minimum dependencies

We list here the versions of the paquages that have been used for development/testing of pyComBat, as well as for writing the documentation.

### pyCombat dependencies

* python 3.6

* numpy 1.16.4

* mpmath 1.1.0

* pandas 0.24.2

### Documentation

* sphinx 2.1.2

## Usage example

Note that you need to download the utils.py script and put it in the same folder as the rest of your code. We are currently working on making on making pyComBat usable as a Python library.

The simplest way of using pyComBat is to first import it, and simply use the pycombat function with default parameters:

```python
from utils import pycombat
pycombat(data,batch)
```

* data: The expression matrix. It contains the information about the gene expression (rows) for each sample (columns). The first column (resp. row) is dedicated for the gene (resp. sample) names.

* batch: List of batch indexes. The batch list describes the batch for each sample. The list of batches contains as many elements as the number of columns in the expression matrix.

## How to contribute

Please refer to [CONTRIBUTING.md](https://github.com/epigenelabs/pyComBat/blob/master/CONTRIBUTING.md) to learn more about the contribution guidelines.

## References

[1]

[2] Johnson,W.E. et al. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118–127