Python NetCDFFile.order方法代码示例

# 需要导入模块: from anuga.file.netcdf import NetCDFFile [as 别名]
# 或者: from anuga.file.netcdf.NetCDFFile import order [as 别名]
def _sww_merge_parallel_non_smooth(swwfiles, output,  verbose=False, delete_old=False):
    """
        Merge a list of sww files into a single file.

        Used to merge files created by parallel runs.

        The sww files to be merged must have exactly the same timesteps.

        It is assumed that the separate sww files have been stored in non_smooth
        format.

        Note that some advanced information and custom quantities may not be
        exported.

        swwfiles is a list of .sww files to merge.
        output is the output filename, including .sww extension.
        verbose True to log output information
    """

    if verbose:
        print "MERGING SWW Files"


    first_file = True
    tri_offset = 0
    for filename in swwfiles:
        if verbose:
            print 'Reading file ', filename, ':'

        fid = NetCDFFile(filename, netcdf_mode_r)

        if first_file:

            times    = fid.variables['time'][:]
            n_steps = len(times)
            number_of_timesteps = fid.dimensions['number_of_timesteps']
            #print n_steps, number_of_timesteps
            starttime = int(fid.starttime)

            out_s_quantities = {}
            out_d_quantities = {}

            out_s_c_quantities = {}
            out_d_c_quantities = {}


            xllcorner = fid.xllcorner
            yllcorner = fid.yllcorner

            number_of_global_triangles = int(fid.number_of_global_triangles)
            number_of_global_nodes     = int(fid.number_of_global_nodes)
            number_of_global_triangle_vertices = 3*number_of_global_triangles


            order      = fid.order
            xllcorner  = fid.xllcorner;
            yllcorner  = fid.yllcorner ;
            zone       = fid.zone;
            false_easting  = fid.false_easting;
            false_northing = fid.false_northing;
            datum      = fid.datum;
            projection = fid.projection;

            g_volumes = num.arange(number_of_global_triangles*3).reshape(-1,3)



            g_x = num.zeros((number_of_global_triangle_vertices,),num.float32)
            g_y = num.zeros((number_of_global_triangle_vertices,),num.float32)

            g_points = num.zeros((number_of_global_triangle_vertices,2),num.float32)

            #=======================================
            # Deal with the vertex based variables
            #=======================================
            quantities = set(['elevation', 'friction', 'stage', 'xmomentum',
                              'ymomentum', 'xvelocity', 'yvelocity', 'height'])
            variables = set(fid.variables.keys())

            quantities = list(quantities & variables)

            static_quantities = []
            dynamic_quantities = []

            for quantity in quantities:
                # Test if elevation is static
                if n_steps == fid.variables[quantity].shape[0]:
                    dynamic_quantities.append(quantity)
                else:
                    static_quantities.append(quantity)

            # Static Quantities are stored as a 1D array
            for quantity in static_quantities:
                out_s_quantities[quantity] = num.zeros((3*number_of_global_triangles,),num.float32)

            #=======================================
            # Deal with the centroid based variables
            #=======================================
            quantities = set(['elevation_c', 'friction_c', 'stage_c', 'xmomentum_c',
                              'ymomentum_c', 'xvelocity_c', 'yvelocity_c', 'height_c'])
#.........这里部分代码省略.........

# 需要导入模块: from anuga.file.netcdf import NetCDFFile [as 别名]
# 或者: from anuga.file.netcdf.NetCDFFile import order [as 别名]
def _sww_merge_parallel_smooth(swwfiles, output,  verbose=False, delete_old=False):
    """
        Merge a list of sww files into a single file.
        
        Use to merge files created by parallel runs.

        The sww files to be merged must have exactly the same timesteps.

        It is assumed that the separate sww files have been stored in non_smooth
        format.

        Note that some advanced information and custom quantities may not be
        exported.
        
        swwfiles is a list of .sww files to merge.
        output is the output filename, including .sww extension.
        verbose True to log output information
    """

    if verbose:
        print "MERGING SWW Files"
        
    
    first_file = True
    tri_offset = 0
    for filename in swwfiles:
        if verbose:
            print 'Reading file ', filename, ':'    
    
        fid = NetCDFFile(filename, netcdf_mode_r)
         
        if first_file:

            times    = fid.variables['time'][:]
            n_steps = len(times)
            #number_of_timesteps = fid.dimensions['number_of_timesteps']
            #print n_steps, number_of_timesteps
            starttime = int(fid.starttime)
            
            out_s_quantities = {}
            out_d_quantities = {}

            out_s_c_quantities = {}
            out_d_c_quantities = {}


            xllcorner = fid.xllcorner
            yllcorner = fid.yllcorner

            number_of_global_triangles = int(fid.number_of_global_triangles)
            number_of_global_nodes     = int(fid.number_of_global_nodes)

            order      = fid.order
            xllcorner  = fid.xllcorner;
            yllcorner  = fid.yllcorner ;
            zone       = fid.zone;
            false_easting  = fid.false_easting;
            false_northing = fid.false_northing;
            datum      = fid.datum;
            projection = fid.projection;

            g_volumes = num.zeros((number_of_global_triangles,3),num.int)
            g_x = num.zeros((number_of_global_nodes,),num.float32)
            g_y = num.zeros((number_of_global_nodes,),num.float32)

            g_points = num.zeros((number_of_global_nodes,2),num.float32)

            #=====================================
            # Deal with the vertex based variables
            #=====================================
            quantities = set(['elevation', 'friction', 'stage', 'xmomentum',
                              'ymomentum', 'xvelocity', 'yvelocity', 'height'])
            variables = set(fid.variables.keys())

            quantities = list(quantities & variables)
            
            static_quantities = []
            dynamic_quantities = []

            for quantity in quantities:
                # Test if quantity is static
                if n_steps == fid.variables[quantity].shape[0]:
                    dynamic_quantities.append(quantity)
                else:
                    static_quantities.append(quantity)
                
            for quantity in static_quantities:
                out_s_quantities[quantity] = num.zeros((number_of_global_nodes,),num.float32)

            # Quantities are stored as a 2D array of timesteps x data.
            for quantity in dynamic_quantities:
                out_d_quantities[quantity] = \
                      num.zeros((n_steps,number_of_global_nodes),num.float32)

            #=======================================
            # Deal with the centroid based variables
            #=======================================
            quantities = set(['elevation_c', 'friction_c', 'stage_c', 'xmomentum_c',
                              'ymomentum_c', 'xvelocity_c', 'yvelocity_c', 'height_c'])
            variables = set(fid.variables.keys())
#.........这里部分代码省略.........

# 需要导入模块: from anuga.file.netcdf import NetCDFFile [as 别名]
# 或者: from anuga.file.netcdf.NetCDFFile import order [as 别名]
def _sww_merge(swwfiles, output, verbose=False):
    """
        Merge a list of sww files into a single file.
        
        May be useful for parallel runs. Note that colinear points and
        edges are not merged: there will essentially be multiple meshes within
        the one sww file.
        
        The sww files to be merged must have exactly the same timesteps. Note
        that some advanced information and custom quantities may not be
        exported.
        
        swwfiles is a list of .sww files to merge.
        output is the output filename, including .sww extension.
        verbose True to log output information
    """

    if verbose:
        print "MERGING SWW Files"
        
    static_quantities = ['elevation']
    dynamic_quantities = ['stage', 'xmomentum', 'ymomentum']
    
    first_file = True
    tri_offset = 0
    for filename in swwfiles:
        if verbose:
            print 'Reading file ', filename, ':'    
    
        fid = NetCDFFile(filename, netcdf_mode_r)



        
        
        tris = fid.variables['volumes'][:]       
         
        if first_file:
            times = fid.variables['time'][:]
            x = []
            y = []
            out_tris = list(tris)  
            out_s_quantities = {}
            out_d_quantities = {}


            xllcorner = fid.xllcorner
            yllcorner = fid.yllcorner

            order      = fid.order
            xllcorner  = fid.xllcorner;
            yllcorner  = fid.yllcorner ;
            zone       = fid.zone;
            false_easting  = fid.false_easting;
            false_northing = fid.false_northing;
            datum      = fid.datum;
            projection = fid.projection;

            
            for quantity in static_quantities:
                out_s_quantities[quantity] = []

            # Quantities are stored as a 2D array of timesteps x data.
            for quantity in dynamic_quantities:
                out_d_quantities[quantity] = [ [] for _ in range(len(times))]
                 
            description = 'merged:' + getattr(fid, 'description')          
            first_file = False
        else:
            for tri in tris:
                # Advance new tri indices to point at newly appended points.
                verts = [vertex+tri_offset for vertex in tri]
                out_tris.append(verts)



        try: # works with netcdf4
            num_pts = len(fid.dimensions['number_of_points'])
        except: # works with scientific.io.netcdf
            num_pts = int(fid.dimensions['number_of_points'])

        tri_offset += num_pts
        
        if verbose:
            print '  new triangle index offset is ', tri_offset
            
        x.extend(list(fid.variables['x'][:]))
        y.extend(list(fid.variables['y'][:]))
        
        # Grow the list of static quantities associated with the x,y points
        for quantity in static_quantities:
            out_s_quantities[quantity].extend(fid.variables[quantity][:])
            
        #Collate all dynamic quantities according to their timestep
        for quantity in dynamic_quantities:
            time_chunks = fid.variables[quantity][:]
            for i, time_chunk in enumerate(time_chunks):
                out_d_quantities[quantity][i].extend(time_chunk)            
    
    # Mash all points into a single big list    
#.........这里部分代码省略.........

# 需要导入模块: from anuga.file.netcdf import NetCDFFile [as 别名]
# 或者: from anuga.file.netcdf.NetCDFFile import order [as 别名]

#.........这里部分代码省略.........
    number_of_latitudes = len(latitudes)
    number_of_longitudes = len(longitudes)
    number_of_times = len(os.listdir(elevation_dir))
    number_of_points = number_of_latitudes * number_of_longitudes
    number_of_volumes = (number_of_latitudes - 1) * (number_of_longitudes - 1) * 2

    # Work out the times
    if len(elevation_files) > 1:
        # Assume: The time period is less than 24hrs.
        time_period = (int(elevation_files[1][-3:]) - int(elevation_files[0][-3:])) * 60 * 60
        times = [x * time_period for x in range(len(elevation_files))]
    else:
        times = [0.0]

    if verbose:
        log.critical("------------------------------------------------")
        log.critical("Statistics:")
        log.critical("  Extent (lat/lon):")
        log.critical("    lat in [%f, %f], len(lat) == %d" % (min(latitudes), max(latitudes), len(latitudes)))
        log.critical("    lon in [%f, %f], len(lon) == %d" % (min(longitudes), max(longitudes), len(longitudes)))
        log.critical("    t in [%f, %f], len(t) == %d" % (min(times), max(times), len(times)))

    ######### WRITE THE SWW FILE #############

    # NetCDF file definition
    outfile = NetCDFFile(sww_file, netcdf_mode_w)

    # Create new file
    outfile.institution = "Geoscience Australia"
    outfile.description = "Converted from XXX"

    # For sww compatibility
    outfile.smoothing = "Yes"
    outfile.order = 1

    # Start time in seconds since the epoch (midnight 1/1/1970)
    outfile.starttime = starttime = times[0]

    # dimension definitions
    outfile.createDimension("number_of_volumes", number_of_volumes)
    outfile.createDimension("number_of_vertices", 3)
    outfile.createDimension("number_of_points", number_of_points)
    outfile.createDimension("number_of_timesteps", number_of_times)

    # variable definitions
    outfile.createVariable("x", precision, ("number_of_points",))
    outfile.createVariable("y", precision, ("number_of_points",))
    outfile.createVariable("elevation", precision, ("number_of_points",))

    # FIXME: Backwards compatibility
    # outfile.createVariable('z', precision, ('number_of_points',))
    #################################

    outfile.createVariable("volumes", netcdf_int, ("number_of_volumes", "number_of_vertices"))

    outfile.createVariable("time", precision, ("number_of_timesteps",))

    outfile.createVariable("stage", precision, ("number_of_timesteps", "number_of_points"))

    outfile.createVariable("xmomentum", precision, ("number_of_timesteps", "number_of_points"))

    outfile.createVariable("ymomentum", precision, ("number_of_timesteps", "number_of_points"))

    # Store
    from anuga.coordinate_transforms.redfearn import redfearn