source: inundation/ga/storm_surge/pyvolution/domain.py @ 469

"""Class Domain - 2D triangular domains for finite-volume computations of
   the shallow water wave equation


   Copyright 2004
   Ole Nielsen, Stephen Roberts, Duncan Gray, Christopher Zoppou
   Geoscience Australia
"""

from mesh import Mesh
from generic_boundary_conditions import *

class Domain(Mesh):

    def __init__(self, coordinates, vertices, boundary = None,
                 conserved_quantities = None, other_quantities = None,
                 tagged_elements = None):

        Mesh.__init__(self, coordinates, vertices, boundary, tagged_elements)

        from Numeric import zeros, Float 
        from quantity import Quantity, Conserved_quantity

        #List of quantity names entering
        #the conservation equations
        #(Must be a subset of quantities)
        if conserved_quantities is None:            
            self.conserved_quantities = []
        else:
            self.conserved_quantities = conserved_quantities            

        if other_quantities is None:
            self.other_quantities = []
        else:
            self.other_quantities = other_quantities            
            

        #Build dictionary of Quantity instances keyed by quantity names
        self.quantities = {}

        #FIXME: remove later
        for name in self.conserved_quantities:
            self.quantities[name] = Conserved_quantity(self)
        for name in self.other_quantities:
            self.quantities[name] = Quantity(self)


        #FIXME: Move these explanations elsewhere    
            
        #Create an empty list for explicit forcing terms
        # 
        # Explicit terms must have the form
        #
        #    G(q, t)
        #
        # and explicit scheme is
        #
        #   q^{(n+1}) = q^{(n)} + delta_t G(q^{n}, n delta_t)
        #
        #
        # FIXME: How to call and how function should look

        self.forcing_terms = []


        #Create an empty list for semi implicit forcing terms if any
        # 
        # Semi implicit forcing terms are assumed to have the form
        #
        #    G(q, t) = H(q, t) q
        #
        # and the semi implicit scheme will then be
        #
        #   q^{(n+1}) = q^{(n)} + delta_t H(q^{n}, n delta_t) q^{(n+1})
        
        ###self.semi_implicit_forcing_terms = []


        #Defaults
        from config import max_smallsteps, beta, epsilon
        self.beta = beta
        self.epsilon = epsilon
        self.default_order = 1
        self.order = self.default_order
        self.smallsteps = 0
        self.max_smallsteps = max_smallsteps        
        self.number_of_steps = 0
        self.number_of_first_order_steps = 0        

        #Model time    
        self.time = 0.0
        self.finaltime = None
        self.min_timestep = self.max_timestep = 0.0        
        
        #Checkpointing          
        self.filename = 'domain'
        self.checkpoint = False
        

    #Public interface to Domain        
    def get_conserved_quantities(self, vol_id, vertex=None, edge=None):
        """Get conserved quantities at volume vol_id

        If vertex is specified use it as index for vertex values
        If edge is specified use it as index for edge values
        If neither are specified use centroid values
        If both are specified an exeception is raised

        Return value: Vector of length == number_of_conserved quantities 
        
        """

        from Numeric import zeros, Float
        
        if not (vertex is None or edge is None):
            msg = 'Values for both vertex and edge was specified.'
            msg += 'Only one (or none) is allowed.' 
            raise msg

        q = zeros( len(self.conserved_quantities), Float)
        
        for i, name in enumerate(self.conserved_quantities):
            Q = self.quantities[name]
            if vertex is not None:
                q[i] = Q.vertex_values[vol_id, vertex]
            elif edge is not None:
                q[i] = Q.edge_values[vol_id, edge]
            else:
                q[i] = Q.centroid_values[vol_id]                

        return q 


    def set_quantity_vertices_dict(self, quantity_dict):
        """Set values for named quantities.
        The index is the quantity
        
        name: Name of quantity
        X: Compatible list, Numeric array, const or function (see below)
        
        The values will be stored in elements following their
        internal ordering.

        """
        for key in quantity_dict.keys():
            self.set_quantity(key, quantity_dict[key], location='vertices')
        
    def set_quantity(self, name, X, location='vertices'):
        """Set values for named quantity
        
        name: Name of quantity
        X: Compatible list, Numeric array, const or function (see below)
        location: Where values are to be stored.
                  Permissible options are: vertices, edges, centroid

        In case of location == 'centroid' the dimension values must
        be a list of a Numerical array of length N, N being the number
        of elements in the mesh. Otherwise it must be of dimension Nx3

        The values will be stored in elements following their
        internal ordering.
        """

        from quantity import Quantity, Conserved_quantity
        
        #Create appropriate quantity object
        ##if name in self.conserved_quantities:
        ##    self.quantities[name] = Conserved_quantity(self)
        ##else:
        ##    self.quantities[name] = Quantity(self)            

        #Set value    
        self.quantities[name].set_values(X, location)
        

    def set_boundary(self, boundary_map):
        """Associate boundary objects with tagged boundary segments.

        Input boundary_map is a dictionary of boundary objects keyed
        by symbolic tags to matched against tags in the internal dictionary
        self.boundary.

        As result one pointer to a boundary object is stored for each vertex
        in the list self.boundary_objects.
        More entries may point to the same boundary object
        
        Schematically the mapping is:

        self.boundary_segments: k: (vol_id, edge_id)
        self.boundary:          (vol_id, edge_id): tag
        boundary_map (input):   tag: boundary_object
        ----------------------------------------------
        self.boundary_objects:  k: boundary_object
        

        Pre-condition:
          self.boundary and self.boundary_segments have been built.

        Post-condition:
          self.boundary_objects is built

        If a tag from the domain doesn't appear in the input dictionary an
        exception is raised.
        However, if a tag is not used to the domain, no error is thrown.
        FIXME: This would lead to implementation of a
        default boundary condition
        """

        self.boundary_objects = []
        for k, (vol_id, edge_id) in enumerate(self.boundary_segments):
            tag = self.boundary[ (vol_id, edge_id) ]

            if boundary_map.has_key(tag):
                B = boundary_map[tag]
                self.boundary_objects.append(B)                

            else:
                msg = 'ERROR (domain.py): Tag "%s" has not been ' %tag
                msg += 'bound to a boundary object.\n'
                msg += 'All boundary tags defined in domain must appear '
                msg += 'in the supplied dictionary.\n'
                msg += 'The tags are: %s' %self.get_boundary_tags()
                raise msg

    def set_region(self, functions):
        # The order of functions in the list is used.
        for function in functions:
            for tag in self.tagged_elements.keys():
                function(tag, self.tagged_elements[tag], self)

                #Do we need to do this sort of thing?
                #self = function(tag, self.tagged_elements[tag], self)
            
    #MISC        
    def check_integrity(self):
        Mesh.check_integrity(self)

        for quantity in self.conserved_quantities:
            msg = 'Conserved quantities must be a subset of all quantities'
            assert quantity in self.quantities, msg
    
    def write_time(self):
        if self.min_timestep == self.max_timestep:
            print 'Time = %.4f, delta t = %.8f, steps=%d (%d)'\
                  %(self.time, self.min_timestep, self.number_of_steps,
                    self.number_of_first_order_steps)
        elif self.min_timestep > self.max_timestep:
            print 'Time = %.4f, steps=%d (%d)'\
                  %(self.time, self.number_of_steps,
                    self.number_of_first_order_steps)            
        else:
            print 'Time = %.4f, delta t in [%.8f, %.8f], steps=%d (%d)'\
                  %(self.time, self.min_timestep,
                    self.max_timestep, self.number_of_steps,
                    self.number_of_first_order_steps)


    def get_name(self):
        return self.filename    
    

    #def set_defaults(self):
    #    """Set default values for uninitialised quantities.
    #    Should be overridden or specialised by specific modules
    #    """#
    #
    #    for name in self.conserved_quantities + self.other_quantities:
    #        self.set_quantity(name, 0.0)        
                

    ###########################
    #Main components of evolve

    def evolve(self, yieldstep = None, finaltime = None):
        """Evolve model from time=0.0 to finaltime yielding results
        every yieldstep.
        
        Internally, smaller timesteps may be taken.

        Evolve is implemented as a generator and is to be called as such, e.g.
        
        for t in domain.evolve(timestep, yieldstep, finaltime):
            <Do something with domain and t>
        
        """

        #import data_manager
        from config import min_timestep, max_timestep, epsilon


        ##self.set_defaults()

        if yieldstep is None:
            yieldstep = max_timestep

        self.order = self.default_order
        
           
        self.yieldtime = 0.0 #Time between 'yields'

        #Initialise interval of timestep sizes (for reporting only)
        self.min_timestep = max_timestep
        self.max_timestep = min_timestep
        self.finaltime = finaltime
        self.number_of_steps = 0
        self.number_of_first_order_steps = 0                

        #Initial update of vertex and edge values
        self.distribute_to_vertices_and_edges()
        
        
        #Or maybe restore from latest checkpoint
        if self.checkpoint is True:
            self.goto_latest_checkpoint()

            
        yield(self.time)  #Yield initial values
        
        while True:
            #Update boundary values
            self.update_boundary()            

            #Compute fluxes across each element edge 
            self.compute_fluxes()

            #Update timestep to fit yieldstep and finaltime
            self.update_timestep(yieldstep, finaltime)

            #Update conserved quantities
            self.update_conserved_quantities()            

            #Update vertex and edge values
            self.distribute_to_vertices_and_edges()
            
            #Update time    
            self.time += self.timestep
            self.yieldtime += self.timestep
            self.number_of_steps += 1
            if self.order == 1:
                self.number_of_first_order_steps += 1        

            #Yield results
            if finaltime is not None and abs(self.time - finaltime) < epsilon:
                # Yield final time and stop
                yield(self.time)
                break

                
            if abs(self.yieldtime - yieldstep) < epsilon: 
                # Yield (intermediate) time and allow inspection of domain

                if self.checkpoint is True:
                    self.store_checkpoint()
                    self.delete_old_checkpoints()
                    
                #Pass control on to outer loop for more specific actions    
                yield(self.time)  

                # Reinitialise
                self.yieldtime = 0.0                
                self.min_timestep = max_timestep
                self.max_timestep = min_timestep
                self.number_of_steps = 0
                self.number_of_first_order_steps = 0                        

            
    def evolve_to_end(self, finaltime = 1.0):
        """Iterate evolve all the way to the end
        """

        for _ in self.evolve(yieldstep=None, finaltime=finaltime):            
            pass
        

    
    def update_boundary(self):
        """Go through list of boundary objects and update boundary values
        for all conserved quantities on boundary.
        """

        #FIXME: Update only those that change (if that can be worked out)
        for i, B in enumerate(self.boundary_objects):
            vol_id, edge_id = self.boundary_segments[i]
            q = B.evaluate(vol_id, edge_id)

            for j, name in enumerate(self.conserved_quantities):
                Q = self.quantities[name]
                Q.boundary_values[i] = q[j]


    def compute_fluxes(self):
        msg = 'Method compute_fluxes must be overridden by Domain subclass'
        raise msg


    def update_timestep(self, yieldstep, finaltime):

        from config import min_timestep
    
        timestep = self.timestep
    
        #Record maximal and minimal values of timestep for reporting    
        self.max_timestep = max(timestep, self.max_timestep)
        self.min_timestep = min(timestep, self.min_timestep)
    
        #Protect against degenerate time steps
        if timestep < min_timestep:

            #Number of consecutive small steps taken b4 taking action
            self.smallsteps += 1 

            if self.smallsteps > self.max_smallsteps:
                self.smallsteps = 0 #Reset
               
                if self.order == 1:
                    msg = 'Too small timestep %.16f reached ' %timestep
                    msg += 'even after %d steps of 1 order scheme'\
                           %self.max_smallsteps

                    raise msg
                else:
                    #Try to overcome situation by switching to 1 order
                    self.order = 1

        else:
            self.smallsteps = 0
            if self.order == 1 and self.default_order == 2:
                self.order = 2
                    

        #Ensure that final time is not exceeded
        if finaltime is not None and self.time + timestep > finaltime:
            timestep = finaltime-self.time

        #Ensure that model time is aligned with yieldsteps
        if self.yieldtime + timestep > yieldstep:
            timestep = yieldstep-self.yieldtime

        self.timestep = timestep



    def compute_forcing_terms(self):          
        """If there are any forcing functions driving the system
        they should be defined in Domain subclass and appended to
        the list self.forcing_terms
        """

        for f in self.forcing_terms:
            f(self)

        

    def update_conserved_quantities(self):
        """Update vectors of conserved quantities using previously
        computed fluxes specified forcing functions.
        """

        from Numeric import ones, sum, equal, Float
            
        N = self.number_of_elements
        d = len(self.conserved_quantities)
        
        timestep = self.timestep

        #Compute forcing terms    
        self.compute_forcing_terms()    

        #Update conserved_quantities
        for name in self.conserved_quantities:
            Q = self.quantities[name]
            Q.update(timestep)

            #Clean up
            #Note that Q.explicit_update is reset by compute_fluxes            
            Q.semi_implicit_update[:] = 0.0

            

    def distribute_to_vertices_and_edges(self):
        """Extrapolate conserved quantities from centroid to
        vertices and edge-midpoints for each volume

        Default implementation is straight first order,
        i.e. constant values throughout each element and
        no reference to non-conserved quantities.
        """

        for name in self.conserved_quantities:
            Q = self.quantities[name]
            if self.order == 1:
                Q.extrapolate_first_order()
            elif self.order == 2:
                Q.extrapolate_second_order()
                Q.limit()                                
            else:
                raise 'Unknown order'
            Q.interpolate_from_vertices_to_edges()                            


                
##############################################
#Initialise module

#Optimisation with psyco
from config import use_psyco
if use_psyco:
    try:
        import psyco
    except:
        msg = 'WARNING: psyco (speedup) could not import'+\
              ', you may want to consider installing it'
        print msg
    else:
        psyco.bind(Domain.update_boundary) 
        #psyco.bind(Domain.update_timestep)     #Not worth it
        psyco.bind(Domain.update_conserved_quantities)
        psyco.bind(Domain.distribute_to_vertices_and_edges)
        

if __name__ == "__main__":
    pass