Metadata-Version: 1.0
Name: spacegrids
Version: 1.6.19
Summary: numpy array with grids and associated operations
Home-page: https://github.com/willo12/spacegrids
Author: Willem Sijp
Author-email: w.sijp@unsw.edu.au
License: BSD
Description: Spacegrids
        ==========
        
        Spacegrids is an open source library providing a Numpy array with grids, labelled axes and associated grid-related mathematical methods such as regridding and integration. Spacegrids provides an object data model of Netcdf data that ensures consistency between a Numpy data array and its grid under common operations (and so avoiding common pitfalls related to axis interpretation), and much more. It is a write less do more library for everyday use.
        
        The Field, Gr (grid) and Coord objects make everyday use easy:
        
            >>> import spacegrids as sg		
            >>> D = sg.info(nonick = True)  
            >>> P = sgPproject(D['my_project'] , nonick = True)  
            >>> P.load(['temperature','u'])  
            >>> # obtain the axes objects under their names T,X,Y,Z: 
            >>> for c in P['some_experiment'].axes:
            >>>   exec c.name + ' = c'	# now we can refer to X,Y
            >>> TEMP = P['some_experiment']['temperature'] 
            >>> U = P['some_experiment']['u'] # zonal velocity
            >>> TEMP_sliced = TEMP[Y,:50] # slice. Note Y axis object
            >>> m_TEMP = TEMP_sliced/(X*Y) # take hor. mean
            >>> TEMP_regridded = TEMP.regrid(U.gr)  # U grid differs
         
        
        Features
        --------
        
        - A numpy array with grid allowing automatic alignment and dimension broadcasting
        - Easy to use and intuitive regridding functionality
        - A data object model corresponding closely to Netcdf
        - Easier IO via abstraction of IO with multiple Netcdf files
        - Makes working with output of many experiments easy via aggregation methods
        - The Field class eliminates errors arising from picking the wrong array index
        - Quicker plotting due to automatic labels, axes etc.
        - Distance-related methods such as spatial differentiation and integration on sphere
        - Extensive unit tests and documentation
        
        There is lots of documentation, both in the source code and elsewhere. Other documentation can be found at: 
        
        - `a practical tutorial <http://nbviewer.ipython.org/github/willo12/spacegrids/blob/master/Spacegrids.ipynb>`_ 
        - `a more advanced tutorial <http://nbviewer.ipython.org/github/willo12/spacegrids/blob/master/advanced.ipynb>`_ 
        - `an overview of all classes, methods and functions <http://web.maths.unsw.edu.au/~wsijp/html/index.html>`_ 
        
        
        Installation
        ------------
        
        Install spacegrids simply by running (on command line):
        
            pip install spacegrids
        
        On Mac, pip can be installed via "sudo easy_install pip". On Ubuntu/ Debian, install dependencies via package manager if pip install fails:
        
            apt-get install python-{tk,numpy,matplotlib,scipy}
        
        
        Contribute
        ----------
        
        - Issue Tracker: github.com/willo12/spacegrids/issues
        - Source Code: github.com/willo12/spacegrids
        
        Support
        -------
        
        If you are having issues, please let us know.
        
        License
        -------
        
        The project is licensed under the BSD license.
        
Keywords: climate data,grid data,data on grids,spatial grids,Netcdf data analysis,climate analysis scripts,interpreting meta data Netcdf,geophysics tools
Platform: UNKNOWN
