Metadata-Version: 2.1
Name: LMRt
Version: 0.6.5
Summary: A lightweight, packaged version of the Last Millennium Reanalysis (LMR) framework
Home-page: https://github.com/fzhu2e/LMRt
Author: Feng Zhu
Author-email: fengzhu@usc.edu
License: MIT license
Description: [![PyPI](https://img.shields.io/pypi/v/LMRt.svg)](https://pypi.org/project/LMRt/)
        [![](https://img.shields.io/badge/platform-Mac_Linux-red.svg)]()
        [![](https://img.shields.io/badge/language-Python3-success.svg)](https://www.python.org/)
        [![](https://img.shields.io/badge/license-BSD-brown.svg)](https://github.com/fzhu2e/LMRt/blob/master/LICENSE)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2655097.svg)](https://doi.org/10.5281/zenodo.2655097)
        
        # LMR Turbo (LMRt)
        
        A lightweight, packaged version of the [Last Millennium Reanalysia (LMR)](https://github.com/modons/LMR) framework,
        inspired by LMR_lite.py originated by Professor Hakim (Univ. of Washington).
        Ultimately, it aims to provide following features:
        
        + Greater flexibility
            + Easy installation
            + Easy importing and usage in Jupyter notebooks (or scripts)
            + No assumption of a fixed folder structure; just feed the correct files to functions
            + Easy setup for different priors, proxies, and Proxy System Models (PSMs) included in [PRYSM API](https://github.com/fzhu2e/prysm-api)
        + Faster speed
            + Easy parallel computing with multiprocessing and other techniques
        + Leveraging the power of Machine Learning (added in v0.6.0)
        
        ## Results
        
        ### Mean temperature
        ![Mean temperature](notebooks/figs/gmt.png)
        
        ### Niño 3.4 index
        ![Niño 3.4](notebooks/figs/nino34.png)
        
        
        ## Package dependencies
        + [tqdm](https://github.com/tqdm/tqdm): A fast, extensible progress bar for Python and CLI (`pip install tqdm`)
        + [prysm-api](https://github.com/fzhu2e/prysm-api): The API for PRoxY System Modeling (PRYSM) (`pip install prysm-api`)
        + [dotmap](https://github.com/drgrib/dotmap): Dot access dictionary with dynamic hierarchy creation and ordered iteration (`pip install dotmap`)
        + [xarray](https://github.com/pydata/xarray): N-D labeled arrays and datasets in Python (`pip install xarray`)
        + [netCDF4](https://github.com/Unidata/netcdf4-python): the python interface for netCDF4 format (`conda install netCDF4`)
        + [pyspharm](https://code.google.com/archive/p/pyspharm/): an  object-oriented python interface to the NCAR SPHEREPACK library (`conda install -c conda-forge pyspharm`)
        + [pyyaml](https://github.com/yaml/pyyaml): The next generation YAML parser and emitter for Python (`pip install pyyaml`).
        + [cartopy](https://scitools.org.uk/cartopy/docs/latest/): a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses (`conda install -c conda-forge cartopy`).
        + [scikit-learn](https://scikit-learn.org/stable/index.html): Machine Learning in Python (`pip install -U scikit-learn`).
        + [keras](http://keras.io/): Deep Learning for humans (`pip install keras`).
        + [tensorflow](https://github.com/tensorflow/tensorflow): An Open Source Machine Learning Framework for Everyone (`pip install tensorflow` or `pip install tensorflow-gpu`).
        
        ## How to install
        Once the above dependencies are installed, simply
        ```bash
        pip install LMRt
        ```
        and you are ready to
        ```python
        import LMRt
        ```
        in python.
        
        ## Notebook tutorials
        + [a quickstart](https://nbviewer.jupyter.org/github/fzhu2e/LMRt/blob/master/notebooks/01.lmrt_quickstart.ipynb)
        + [building Ye files](https://nbviewer.jupyter.org/github/fzhu2e/LMRt/blob/master/notebooks/02.build_Ye.ipynb)
        + [multiprocessing](https://nbviewer.jupyter.org/github/fzhu2e/LMRt/blob/master/notebooks/03.multiprocessing.ipynb)
        
        ## References
        + Hakim, G. J., J. Emile‐Geay, E. J. Steig, D. Noone, D. M. Anderson, R. Tardif, N. Steiger, and W. A. Perkins, 2016: The last millennium climate reanalysis project: Framework and first results. Journal of Geophysical Research: Atmospheres, 121, 6745–6764, https://doi.org/10.1002/2016JD024751.
        + Tardif, R., Hakim, G. J., Perkins, W. A., Horlick, K. A., Erb, M. P., Emile-Geay, J., Anderson, D. M., Steig, E. J., and Noone, D.: Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling, Clim. Past Discuss., https://doi.org/10.5194/cp-2018-120, in review, 2018.
        
        ## License
        BSD License (see the details [here](LICENSE))
        
        ## How to cite
        If you find this package useful, please cite it with DOI: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2655097.svg)](https://doi.org/10.5281/zenodo.2655097)
        
        ... and welcome to Star and Fork!
        
        
Keywords: Paleoclimate Data Assimilation
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
