MultiModalAnalysis is a package to easily postprocess GIWAXS data.
Project description
Multi Modal Analysis
This repository contains a Python script designed for analysis tasks related to MMAnalysis. The script performs various data processing tasks including logging data selection, GIWAXS data selection, and PL data selection. It also allows for peak fitting and generates stacked plots.
Requirements
Check the file requirements.txt to see which packages are needed. Installing the package using pip
should already take care of all dependencies.
Installation instructions
Create a new virtual environment
Create a new Python environment. (You can also do it in a pre-existing environment, but make sure you don't break something):
conda create -n mmanalysis python=3.11
conda activate mmanalysis
Note that you may need to initialize your shell within conda, e.g., using conda init bash. You will know if the conda environment has been activated when you see that your shell prompt is modified with (mmanalysis
).
After activating your new (or existing) environment, follow the next steps.
Install using pip
You can simply install the latest release of the package and all dependencies using:
pip install mmanalysis
Install directly the source code
Alternatively you can obtain mmanalysis
directly from the repository by following those steps:
Clone the repository in the desired location:
git clone https://github.com/sutterfellalab/MultiModalAnalysis.git
Install the required packages:
cd MultiModalAnalysis
conda install -c conda-forge --file requirements.txt
Install the package with pip:
pip install .
Features
- Logging Data Selection: Automatically suggests start times and plots raw and post-processed log data.
- GIWAXS Data Selection: Automatically finds start times, plots raw and post-processed GIWAXS data, and performs peak fitting.
- PL Data Selection: Plots raw and post-processed PL data, optimizes data for plotting, and performs peak fitting.
- Stacked Plots: Generates stacked plots for combined GIWAXS, PL, and logging data.
Contact
Feel free to create Merge Requests and Issues on our GitHub page: https://github.com/sutterfellalab/MultiModalAnalysis.
If you want to contact the authors, please write to T. Kodalle at TimKodalle@lbl.gov.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file mmanalysis-0.0.1.tar.gz
.
File metadata
- Download URL: mmanalysis-0.0.1.tar.gz
- Upload date:
- Size: 23.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c84f1c95603a198b33f987be4d1b301f08e159e683bef37301fd1b67fc0d511c |
|
MD5 | 20be4fd947102a56d793daaf7eb7a1cd |
|
BLAKE2b-256 | 2608c6e543b04497b8b54a4c9914dfdf38213039fa5bd131fa67b3f2acec3aae |
File details
Details for the file mmanalysis-0.0.1-py3-none-any.whl
.
File metadata
- Download URL: mmanalysis-0.0.1-py3-none-any.whl
- Upload date:
- Size: 25.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.12.2
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1d0294c2c33ad6dbbc44be3d688992689b374a9dc91e3fb3edc7f5803cc345c2 |
|
MD5 | e0cd44a61f566274e102711a061f2e72 |
|
BLAKE2b-256 | 0e284372384bda4d201c471f57d42f32e7d6e2ca7ab9b370577fcd518d0dcee6 |