{
    "info": {
        "author": "Julien Seguinot",
        "author_email": "seguinot@vaw.baug.ethz.ch",
        "bugtrack_url": null,
        "classifiers": [],
        "description": "PyPDD\n=====\n\n.. image:: https://img.shields.io/pypi/v/pypdd.svg\n   :target: https://pypi.python.org/pypi/pypdd\n.. image:: https://img.shields.io/pypi/l/pypdd.svg\n   :target: https://www.gnu.org/licenses/gpl-3.0.txt\n.. image:: https://zenodo.org/badge/8483394.svg\n   :target: https://zenodo.org/badge/latestdoi/8483394\n\nA Python positive degree day model for glacier surface mass balance.\n\nThis module provides a simple model to compute accumulation and melt on a\nglacier using near-surface air temperature and precipitation time series. The\nmodel assumes that melt is proportional to the number of positive degree-days,\nwhich corresponds to the integral of temperature above 0\u00b0C. Temperature\nvariability is included by assuming a normal temperature distribution around the\nmean. The model optionally includes refreezing of melted snow and ice at the\nglacier surface.\n\nPyPDD_ can be used as a module within Python to operate on Numpy_ arrays. In\naddition, it reads and writes netCDF_ files directly from the command line, and\nprovides a raster module for `GRASS GIS`_. The PDD model is based on an\nalgorithm that was initially developed for the `Parallel Ice Sheet Model`_ and\nadopted here with very few changes.\n\nInstallation::\n\n   pip install pypdd\n\n\nThe PDDModel class\n------------------\n\n**Requires:** NumPy_, SciPy_.\n\nA PDD model instance can be created by::\n\n   from pypdd import PDDModel\n   pdd = PDDModel()\n\nSeveral model parameters can be set at initialization. See ``help(PDDModel)``\nfor a list. Provided two arrays ``temp`` and ``prec`` of shape ``(t, x, y)``\ncontaining temperature and precipitation data, the PDD model can be called\nwith::\n\n   pdd(temp, prec)\n\nThis will return a dictionary containing a number of two- and three-dimensional\narrays, including the number of positive degree day ``'pdd'`` and total surface\nmass balance ``'smb'``. Temperature variability can be included in a third array\n``stdv`` containing temperature standard deviation values::\n\n\tpdd(temp, prec, stdv)\n\nIf any of ``temp``, ``prec``, or ``stdv`` has shape ``(x, y)``, it will be\ninterpreted as constant in time and expanded along the time dimension. Floats\nwith be interpreted as constant in time and space and expanded along all\ndimensions.\n\nNetCDF interface\n----------------\n\n**Requires:** netCDF4-Python_.\n\nThe PDDModel class holds a netCDF operator, which can be called by::\n\n   pdd.nco('input.nc', 'output.nc')\n\nThe file ``'input.nc'`` should contain temperatures and precipitation in\nvariables ``'temp'`` and ``'prec'``. The calculated number of positive degree\ndays and total surface mass balance are stored in variables ``'pdd'`` and\n``'smb'`` of ``'output.nc'``. Keyword argument ``output_size`` or\n``output_variables`` can be used to produce more output.\n\nThe netCDF interface can be used directly from the command line by executing the\nmodule as a script::\n\n   python pypdd.py -i 'input.nc' -o 'output.nc'\n\nIf no input file is provided, an artificial climate will be generated under\n``atm.nc`` and used by the model. By default, output is saved as ``smb.nc``.\nMany more command-line options are available. For an overview type::\n\n   python pypdd.py --help\n\n\nGRASS GIS interface\n-------------------\n\n**Requires:** `GRASS GIS`_.\n\nPyPDD can also operate on GRASS raster maps using the attached module ``r.pdd``.\nTemperature, precipitation and standard deviation maps should be provided as\ncomma-separated lists::\n\n   r.pdd.py temp=list,of,temp,maps prec=list,of,prec,maps pdd=pdd_map smb=smb_map\n\nAll time-independent PyPDD output variables can currently be exported as raster\nmaps. Alike any other GRASS module, a graphical prompt can be invoked by calling\n``r.pdd`` without arguments, and a list of options can be obtained with::\n\n   r.pdd.py --help\n\n\nReferences\n----------\n\nApplications of PyPDD:\n\n* N. Gandy, L. J. Gregoire, J. C. Ely, C. D. Clark, D. M. Hodgson, V. Lee,\n  T. Bradwell, and R. F. Ivanovic.\n  Marine ice sheet instability and ice shelf buttressing of the Minch Ice\n  Stream, northwest Scotland.\n  *The Cryosphere*, 12(11):3635--3651,\n  https://doi.org/10.5194/tc-12-3635-2018, 2018.\n\n* A. Plach, K. H. Nisancioglu, S. Le clec'h, A. Born, P. M. Langebroek, C. Guo,\n  M. Imhof, and T. F. Stocker.\n  Eemian Greenland SMB strongly sensitive to model choice.\n  *Clim. Past*, 14:1463--1485,\n  https://doi.org/10.5194/cp-14-1463-2018, 2018.\n\n* J.\u00a0Seguinot.\n  Spatial and seasonal effects of temperature variability in a positive\n  degree-day glacier surface mass-balance model.\n  *J. Glaciol.*, 59(218):1202--1204,\n  http://doi.org/10.3189/2013JoG13J081, 2013.\n\n\n.. links\n\n.. _GRASS GIS: http://grass.osgeo.org\n.. _netCDF: http://www.unidata.ucar.edu/software/netcdf\n.. _netCDF4-Python: https://github.com/Unidata/netcdf4-python\n.. _NumPy: http://numpy.scipy.org\n.. _Parallel Ice Sheet Model: http://www.pism-docs.org\n.. _PyPDD: https://github.com/jsegu/pypdd\n.. _SciPy: http://www.scipy.org\n\n\n",
        "description_content_type": "text/x-rst",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "http://github.com/juseg/aftershocks",
        "keywords": "",
        "license": "gpl-3.0",
        "maintainer": "",
        "maintainer_email": "",
        "name": "pypdd",
        "package_url": "https://pypi.org/project/pypdd/",
        "platform": "",
        "project_url": "https://pypi.org/project/pypdd/",
        "project_urls": {
            "Homepage": "http://github.com/juseg/aftershocks"
        },
        "release_url": "https://pypi.org/project/pypdd/0.3.1/",
        "requires_dist": [
            "numpy",
            "scipy",
            "netCDF4 ; extra == 'netcdf_interface'"
        ],
        "requires_python": "",
        "summary": "A positive degree day model for glacier surface mass balance",
        "version": "0.3.1"
    },
    "last_serial": 5912963,
    "releases": {
        "0.3.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "252a9183c09b3b71518017ebd3508bab",
                    "sha256": "95c6da7ad9b3de64d445af961299b3b34f31dbdb9c3a39430ac881b18b02d022"
                },
                "downloads": -1,
                "filename": "pypdd-0.3.0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "252a9183c09b3b71518017ebd3508bab",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": null,
                "size": 28169,
                "upload_time": "2018-10-31T06:50:05",
                "url": "https://files.pythonhosted.org/packages/bd/92/a2707aabfdcb46a37f6ff7e3e2607474669a903921e75e95124030d32fa7/pypdd-0.3.0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "5c0034c5a66e62bf5e9d8b3e746bdcf1",
                    "sha256": "92c03e209a5d5334cb6b77ec5b37fb262f71b5fd1dbf0094c3f3c4dc3cb801ad"
                },
                "downloads": -1,
                "filename": "pypdd-0.3.0.tar.gz",
                "has_sig": false,
                "md5_digest": "5c0034c5a66e62bf5e9d8b3e746bdcf1",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 9410,
                "upload_time": "2018-10-31T06:50:07",
                "url": "https://files.pythonhosted.org/packages/89/59/eeb623e5e363eea665cd175386c8dda425ff04d8b0a374742865874a167d/pypdd-0.3.0.tar.gz"
            }
        ],
        "0.3.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "23cb8930a4427febf9b8b60250ac0766",
                    "sha256": "61c00f0bd383e715df9e981a877973bc532218b854d0a1488af940e376058690"
                },
                "downloads": -1,
                "filename": "pypdd-0.3.1-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "23cb8930a4427febf9b8b60250ac0766",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": null,
                "size": 28279,
                "upload_time": "2019-10-01T15:24:34",
                "url": "https://files.pythonhosted.org/packages/87/8d/c889c703010ddc6b2832ebfffc012db2a1d05c73dadf74ca2a488a5df171/pypdd-0.3.1-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "a9b8bcbbad33779a2ad839b9e5f2ab3f",
                    "sha256": "a12f3b7cab7d3190c5f9ae72333f6e4402ba9b0d885e6efaf2a512c287da06c0"
                },
                "downloads": -1,
                "filename": "pypdd-0.3.1.tar.gz",
                "has_sig": false,
                "md5_digest": "a9b8bcbbad33779a2ad839b9e5f2ab3f",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 9515,
                "upload_time": "2019-10-01T15:24:44",
                "url": "https://files.pythonhosted.org/packages/f6/83/6f3c86c30f75d2201a2284bb45b0e7530b0d62c8d65925d2738f86a0a83c/pypdd-0.3.1.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "23cb8930a4427febf9b8b60250ac0766",
                "sha256": "61c00f0bd383e715df9e981a877973bc532218b854d0a1488af940e376058690"
            },
            "downloads": -1,
            "filename": "pypdd-0.3.1-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "23cb8930a4427febf9b8b60250ac0766",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 28279,
            "upload_time": "2019-10-01T15:24:34",
            "url": "https://files.pythonhosted.org/packages/87/8d/c889c703010ddc6b2832ebfffc012db2a1d05c73dadf74ca2a488a5df171/pypdd-0.3.1-py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "a9b8bcbbad33779a2ad839b9e5f2ab3f",
                "sha256": "a12f3b7cab7d3190c5f9ae72333f6e4402ba9b0d885e6efaf2a512c287da06c0"
            },
            "downloads": -1,
            "filename": "pypdd-0.3.1.tar.gz",
            "has_sig": false,
            "md5_digest": "a9b8bcbbad33779a2ad839b9e5f2ab3f",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 9515,
            "upload_time": "2019-10-01T15:24:44",
            "url": "https://files.pythonhosted.org/packages/f6/83/6f3c86c30f75d2201a2284bb45b0e7530b0d62c8d65925d2738f86a0a83c/pypdd-0.3.1.tar.gz"
        }
    ]
}