Metadata-Version: 2.1
Name: pypdd
Version: 0.3.1
Summary: A positive degree day model for glacier surface mass balance
Home-page: http://github.com/juseg/aftershocks
Author: Julien Seguinot
Author-email: seguinot@vaw.baug.ethz.ch
License: gpl-3.0
Description: PyPDD
        =====
        
        .. image:: https://img.shields.io/pypi/v/pypdd.svg
           :target: https://pypi.python.org/pypi/pypdd
        .. image:: https://img.shields.io/pypi/l/pypdd.svg
           :target: https://www.gnu.org/licenses/gpl-3.0.txt
        .. image:: https://zenodo.org/badge/8483394.svg
           :target: https://zenodo.org/badge/latestdoi/8483394
        
        A Python positive degree day model for glacier surface mass balance.
        
        This module provides a simple model to compute accumulation and melt on a
        glacier using near-surface air temperature and precipitation time series. The
        model assumes that melt is proportional to the number of positive degree-days,
        which corresponds to the integral of temperature above 0°C. Temperature
        variability is included by assuming a normal temperature distribution around the
        mean. The model optionally includes refreezing of melted snow and ice at the
        glacier surface.
        
        PyPDD_ can be used as a module within Python to operate on Numpy_ arrays. In
        addition, it reads and writes netCDF_ files directly from the command line, and
        provides a raster module for `GRASS GIS`_. The PDD model is based on an
        algorithm that was initially developed for the `Parallel Ice Sheet Model`_ and
        adopted here with very few changes.
        
        Installation::
        
           pip install pypdd
        
        
        The PDDModel class
        ------------------
        
        **Requires:** NumPy_, SciPy_.
        
        A PDD model instance can be created by::
        
           from pypdd import PDDModel
           pdd = PDDModel()
        
        Several model parameters can be set at initialization. See ``help(PDDModel)``
        for a list. Provided two arrays ``temp`` and ``prec`` of shape ``(t, x, y)``
        containing temperature and precipitation data, the PDD model can be called
        with::
        
           pdd(temp, prec)
        
        This will return a dictionary containing a number of two- and three-dimensional
        arrays, including the number of positive degree day ``'pdd'`` and total surface
        mass balance ``'smb'``. Temperature variability can be included in a third array
        ``stdv`` containing temperature standard deviation values::
        
        	pdd(temp, prec, stdv)
        
        If any of ``temp``, ``prec``, or ``stdv`` has shape ``(x, y)``, it will be
        interpreted as constant in time and expanded along the time dimension. Floats
        with be interpreted as constant in time and space and expanded along all
        dimensions.
        
        NetCDF interface
        ----------------
        
        **Requires:** netCDF4-Python_.
        
        The PDDModel class holds a netCDF operator, which can be called by::
        
           pdd.nco('input.nc', 'output.nc')
        
        The file ``'input.nc'`` should contain temperatures and precipitation in
        variables ``'temp'`` and ``'prec'``. The calculated number of positive degree
        days and total surface mass balance are stored in variables ``'pdd'`` and
        ``'smb'`` of ``'output.nc'``. Keyword argument ``output_size`` or
        ``output_variables`` can be used to produce more output.
        
        The netCDF interface can be used directly from the command line by executing the
        module as a script::
        
           python pypdd.py -i 'input.nc' -o 'output.nc'
        
        If no input file is provided, an artificial climate will be generated under
        ``atm.nc`` and used by the model. By default, output is saved as ``smb.nc``.
        Many more command-line options are available. For an overview type::
        
           python pypdd.py --help
        
        
        GRASS GIS interface
        -------------------
        
        **Requires:** `GRASS GIS`_.
        
        PyPDD can also operate on GRASS raster maps using the attached module ``r.pdd``.
        Temperature, precipitation and standard deviation maps should be provided as
        comma-separated lists::
        
           r.pdd.py temp=list,of,temp,maps prec=list,of,prec,maps pdd=pdd_map smb=smb_map
        
        All time-independent PyPDD output variables can currently be exported as raster
        maps. Alike any other GRASS module, a graphical prompt can be invoked by calling
        ``r.pdd`` without arguments, and a list of options can be obtained with::
        
           r.pdd.py --help
        
        
        References
        ----------
        
        Applications of PyPDD:
        
        * N. Gandy, L. J. Gregoire, J. C. Ely, C. D. Clark, D. M. Hodgson, V. Lee,
          T. Bradwell, and R. F. Ivanovic.
          Marine ice sheet instability and ice shelf buttressing of the Minch Ice
          Stream, northwest Scotland.
          *The Cryosphere*, 12(11):3635--3651,
          https://doi.org/10.5194/tc-12-3635-2018, 2018.
        
        * A. Plach, K. H. Nisancioglu, S. Le clec'h, A. Born, P. M. Langebroek, C. Guo,
          M. Imhof, and T. F. Stocker.
          Eemian Greenland SMB strongly sensitive to model choice.
          *Clim. Past*, 14:1463--1485,
          https://doi.org/10.5194/cp-14-1463-2018, 2018.
        
        * J. Seguinot.
          Spatial and seasonal effects of temperature variability in a positive
          degree-day glacier surface mass-balance model.
          *J. Glaciol.*, 59(218):1202--1204,
          http://doi.org/10.3189/2013JoG13J081, 2013.
        
        
        .. links
        
        .. _GRASS GIS: http://grass.osgeo.org
        .. _netCDF: http://www.unidata.ucar.edu/software/netcdf
        .. _netCDF4-Python: https://github.com/Unidata/netcdf4-python
        .. _NumPy: http://numpy.scipy.org
        .. _Parallel Ice Sheet Model: http://www.pism-docs.org
        .. _PyPDD: https://github.com/jsegu/pypdd
        .. _SciPy: http://www.scipy.org
        
Platform: UNKNOWN
Description-Content-Type: text/x-rst
Provides-Extra: NetCDF interface
