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Dispersion and Transport","monthly_downloads":38,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"# Image-Processing based Atmospheric River Tracking (IPART) algorithms\n\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.02407/status.svg)](https://doi.org/10.21105/joss.02407)\n\n## Introduction\n\nIPART (Image-Processing based Atmospheric River Tracking) is a Python package\nfor automated Atmospheric River (AR) detection, axis finding and AR tracking\nfrom gridded Integrated Vapor Transport (IVT) data, for instance Reanalysis\ndatasets, or model simulations.\n\nIPART is intended for researchers and students who are interested in the\nfield of atmospheric river studies in the present day climate or future\nprojections. Unlike the convectional detection methods that rely on magnitude\nthresholding on the intensities of atmospheric vapor fluxes, IPART tackles the\ndetection task from a spatio-temporal scale perspective and is thus\nfree from magnitude thresholds.\n\n## Documentation\n\nFurther documentation can be found at [https://ipart.readthedocs.io/en/latest/](https://ipart.readthedocs.io/en/latest/).\nA description of the methods is given in this work: [Xu, G., Ma, X., Chang, P., and Wang, L.: Image-processing-based atmospheric river tracking method version 1 (IPART-1), Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, 2020.](https://doi.org/10.5194/gmd-13-4639-2020).\n\n\n## Example use case\n\n\n| ![fig3](joss/fig3.png) |\n| :--: |\n|*(a) The IVT field in kg/m/s at 1984-01-26 00:00 UTC over the North Hemisphere. (b) the IVT reconstruction field (IVT_rec) at the same time point. (c) the IVT anomaly field (IVT_ano) from the THR process at the same time point.*|\n\n| ![](joss/ar_track_198424.png) |\n| :--: |\n|*Locations of a track labelled \"198424\" found in year 1984. Black to yellow color scheme indicates the evolution.*|\n\n\n\n## Dependencies\n\n* Python2.7 or Python3.7.\n* netCDF4 (tested 1.4.2, 1.5.3 in py2, tested 1.5.3 in py3)\n* numpy (developed in 1.16.5 in py2, tested 1.18.1, 1.19.0 in py3)\n* scipy (developed in 1.2.1 in py2, tested 1.4.1, 1.5.1 in py3)\n* matplotlib (tested 2.2.5 in py2, tested 3.3.1 in py3)\n* pandas (developed in 0.23.4, 0.24.2 in py2, tested 1.0.3, 1.0.5 in py3)\n* networkx (developed in 1.11 and 2.2 in py2, tested 2.4 in py3)\n* scikit-image (developed in 0.14.2, 0.14.3 in py2, tested 0.16.2, 0.17.2 in py3)\n* cartopy (optional, only used for plotting. Tested 0.17.0 in py2, tested 1.18.0 in py3)\n* opencv (optional but recommended. Tested 4.5.5 in py3. Used to speed up some computations, new in v3.2.0)\n* OS: Linux or Mac, may work in Windows.\n\n## Installation\n\nRecommend building the Python environment using [Anaconda](https://www.anaconda.com/distribution/).\n\n\n### Install from conda-forge\n\nIn your working Python environment:\n\n```\nconda install -c conda-forge ipart\n```\n\nwill install `ipart` and its dependencies for Python 3.\n\n\n### Create conda environment using environment file\n\nThis way will install the optional `cartopy` package and allow you to run\nthe notebook examples.\n\nAfter Anaconda installation, git clone this repository:\n\n```\ngit clone https://github.com/ihesp/IPART\n```\n\nThen build a new conda environment using the environment file provided. For example:\n\n```\ncd IPART\nconda env create -f environment_py3.yml\n```\n\nThis creates a new environment named `ipartpy3`. Activate the environment using\n\n```\nconda activate ipartpy3\n```\n\nAfter that, you can check the list of packages installed by\n\n```\nconda list\n```\n\nSimilarly for Python 2.7, use\n\n```\nconda env create -f environment_py2.yml\n```\n\nFinally install IPART using:\n\n```\npip install -e .\n```\n\n\n## tests\n\nTo validate installation, issue a new Python session and run\n\n```\nimport ipart\n```\n\nIf nothing prints out, installation is successful.\n\nThe `tests` folder also contains a number of `unittest`s, to run them (only if you have done a source code install):\n\n```\npython -m unittest discover -s tests\n```\n\n\n\n## Inventory\n\n* docs: readthedocs documentation.\n* ipart: core module functions.\n* notebooks: a series of jupyter notebooks illustrating the major functionalities of the package.\n* scripts: example computation scripts. Can be used as templates to quickly develop your own working scripts.\n\n\n## Changelog\n\n### v3.5.0\n\nMinor fix:\n\n* When data resolution is higher than 1.0 degree, put axis-finding using down-sampled AR mask in a `try` block. If it failed, revert back to axis-finding using original resolution.\n\n### v3.4.0\n\nMinor fixes:\n\n* fix a bug in latitudinal range filtering when data cover both of the Northern and Southern Hemispheres.\n* more robust handling of zonally cyclic data.\n* (related to a change in v3.3.0) a better way to prevent potential [matplotlib memory leaking](https://github.com/matplotlib/matplotlib/issues/20490).\n\n\n### v3.3.0\n\n* Minor fixes\n\nUse `agg` backend of `matplotlib` in `utils/funcs.py` to prevent [memory leaking](https://github.com/matplotlib/matplotlib/issues/20490).\n\nAllow specifying the calendar type (e.g. `noleap`) when reading netCDF data using `readNC()`:\n`readNC(data_path, varid, calendar='noleap')`.\n\n### v3.2.0\n\n* Speed optimization for the AR detection task.\n\nFor computations in `scripts/detect_ARs.py` and\n`scripts/detect_ARs_generator_version.py`, expect to see a 200 - 300 % speed up\n(only when the data resolution is higher than 1.0 degree latitude/longitude).\n\nIf the `opencv` module is also installed, up to 300 - 500 % speed gain\n(tested with 0.25 degree resolution data).\n\n### v3.0\n\nMake algorithms zonally cyclic.\n\n### v2.0\n\n* restructure into a module `ipart`, separate module from scripts.\n* add a `findARsGen()` generator function to yield results at each time point separately.\n\n### v1.0\n\n* initial upload. 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