/[escript]/trunk/downunder/py_src/datasources.py
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revision 4041 by caltinay, Sun Oct 28 23:42:35 2012 UTC revision 4115 by gross, Fri Dec 14 04:48:48 2012 UTC
# Line 22  __license__="""Licensed under the Open S Line 22  __license__="""Licensed under the Open S
22  http://www.opensource.org/licenses/osl-3.0.php"""  http://www.opensource.org/licenses/osl-3.0.php"""
23  __url__="https://launchpad.net/escript-finley"  __url__="https://launchpad.net/escript-finley"
24    
25  __all__ = ['DataSource','UBCDataSource','ERSDataSource','SyntheticDataSource','SmoothAnomaly']  __all__ = ['simpleGeoMagneticFluxDensity', 'DataSource','ErMapperData', 'SyntheticDataBase' , 'SyntheticFeatureData', 'SyntheticData','SmoothAnomaly']
26    
27  import logging  import logging
28  import numpy as np  import numpy as np
29  from esys.escript import ReducedFunction, Scalar  from esys.escript import ReducedFunction
30  from esys.escript.linearPDEs import LinearSinglePDE  from esys.escript.linearPDEs import LinearSinglePDE
31  from esys.escript.util import *  from esys.escript.util import *
32  import esys.escript.unitsSI as U  import esys.escript.unitsSI as U
33  from esys.ripley import Brick, Rectangle, ripleycpp  from esys.ripley import Brick, Rectangle, ripleycpp
 import sys  
   
 if sys.version_info[0]>2:  
     xrange=range  
34    
35  try:  try:
36      from scipy.io.netcdf import netcdf_file      from scipy.io.netcdf import netcdf_file
37      __all__ += ['NetCDFDataSource']      __all__ += ['NetCdfData']
38  except:  except:
39      pass      pass
40    
# Line 82  def LatLonToUTM(lon, lat, wkt_string=Non Line 78  def LatLonToUTM(lon, lat, wkt_string=Non
78      x,y=pyproj.transform(p_src, p_dest, lon, lat)      x,y=pyproj.transform(p_src, p_dest, lon, lat)
79      return x,y      return x,y
80    
81    def simpleGeoMagneticFluxDensity(latitude, longitude=0.):
82            theta = (90-latitude)/180.*np.pi
83            B_0=U.Mu_0  * U.Magnetic_Dipole_Moment_Earth / (4 * np.pi *  U.R_Earth**3)
84            B_theta= B_0 * sin(theta)
85            B_r= 2 * B_0 * cos(theta)
86            return B_r, B_theta, 0.
87    
88  class DataSource(object):  class DataSource(object):
89      """      """
90      A class that provides survey data for the inversion process.      A class that provides survey data for the inversion process.
91      This is an abstract base class that implements common functionality.      This is an abstract base class that implements common functionality.
92      Methods to be overwritten by subclasses are marked as such.      Methods to be overwritten by subclasses are marked as such.
93        This class assumes 2D data which is mapped to a slice of a 3D domain.
94        For other setups override the methods as required.
95      """      """
96      # this is currently specific to gravity inversion and should be generalised  
97        GRAVITY, MAGNETIC = list(range(2))
98    
99      def __init__(self):      def __init__(self):
100          """          """
101          Constructor. Sets some defaults and initializes logger.          Constructor. Sets some defaults and initializes logger.
102          """          """
         self._constrainBottom=False  
         self._constrainSides=True  
         self._domain=None  
         self.__set_density_mask=None  
         self.__set_susceptibility_mask=None  
         self.setPadding()  
103          self.logger = logging.getLogger('inv.%s'%self.__class__.__name__)          self.logger = logging.getLogger('inv.%s'%self.__class__.__name__)
104            self.__subsampling_factor=1
105      def setPadding(self, pad_x=10, pad_y=10):          self.__background_magnetic_field=None
         """  
         Sets the amount of padding around the dataset. If `pad_x`/`pad_y`  
         is >=1 the value is treated as number of elements to be added to the  
         domain (per side).  
         If ``0 < pad_x,pad_y < 1``, the padding amount is relative to the  
         dataset size. For example, calling ``setPadding(3, 0.1)`` to a data  
         source with size 10x20 will result in the padded data set size  
         16x24 (10+2*3, 20*(1+2*0.1))  
   
         :param pad_x: Padding per side in x direction (default: 10 elements)  
         :type pad_x: ``int`` or ``float``  
         :param pad_y: Padding per side in y direction (default: 10 elements).  
                       This value is only used for 3-dimensional datasets  
         :type pad_y: ``int`` or ``float``  
         """  
         self._pad_x=pad_x  
         self._pad_y=pad_y  
   
     def setConstraints(self, bottom=False, sides=True):  
         """  
         If `bottom` is True, then the density mask will be set to 1 in the  
         padding area at the bottom of the domain. By default this area is  
         unconstrained. Similarly, if `sides` is True (default) then the  
         horizontal padding area is constrained, otherwise not.  
   
         :param bottom: Whether to constrain the density at the bottom of the  
                        domain  
         :type bottom: ``bool``  
         :param sides: Whether to constrain the density in the padding area  
                       surrounding the data  
         :type sides: ``bool``  
         """  
         self._constrainBottom=bottom  
         self._constrainSides=sides  
   
     def getDomain(self):  
         """  
         Returns a domain that spans the data area plus padding.  
         The domain is created the first time this method is called, subsequent  
         calls return the same domain so anything that affects the domain  
         (such as padding) needs to be set beforehand.  
   
         :return: The escript domain for this data source.  
         :rtype: `esys.escript.Domain`  
         """  
         if self._domain is None:  
             self._domain=self._createDomain(self._pad_x, self._pad_y)  
         return self._domain  
   
     def getSetDensityMask(self):  
         """  
         Returns the density mask data object, where mask has value 1 in the  
         padding area, 0 elsewhere.  
   
         :return: The mask for the density.  
         :rtype: `esys.escript.Data`  
         """  
         return self.__set_density_mask  
           
     def setSetDensityMask(self, mask):  
         self.__set_density_mask=mask  
       
     def getSetSusceptibilityMask(self):  
         """  
         Returns the susceptibility mask data object, where mask has value 1 in the  
         padding area, 0 elsewhere.  
   
         :return: The mask for the susceptibility.  
         :rtype: `esys.escript.Data`  
         """  
         return self.__set_susceptibility_mask  
           
     def setSetSusceptibilityMask(self, mask):  
         self.__set_susceptibility_mask=mask  
           
   
     def getGravityAndStdDev(self):  
         """  
         Returns the gravity anomaly and standard deviation data objects as a  
         tuple. This method must be implemented in subclasses.  
         """  
         raise NotImplementedError  
           
     def getMagneticFlieldAndStdDev(self):  
         """  
         Returns the magnetic flield and standard deviation data objects as a  
         tuple. This method must be implemented in subclasses.  
         """  
         raise NotImplementedError  
       
       
       
     def getBackgroundMagneticField(self):  
         """  
         returns the back ground magnetic field. his method must be implemented in subclasses.  
         """  
         return NotImplementedError  
       
106    
107      def getDataExtents(self):      def getDataExtents(self):
108          """          """
# Line 214  class DataSource(object): Line 116  class DataSource(object):
116          """          """
117          raise NotImplementedError          raise NotImplementedError
118    
119      def getVerticalExtents(self):      def getDataType(self):
         """  
         returns a tuple ``(z0, nz, dz)``, where  
   
         - ``z0`` = minimum z coordinate (origin)  
         - ``nz`` = number of nodes in z direction  
         - ``dz`` = spacing of nodes (= cell size in z)  
   
         This method must be implemented in subclasses.  
         """  
         raise NotImplementedError  
   
     def getDomainClass(self):  
120          """          """
121          returns the domain generator class (e.g. esys.ripley.Brick).          Returns the type of survey data managed by this source.
122          Must be implemented in subclasses.          Subclasses must return `GRAVITY` or `MAGNETIC` as appropriate.
123          """          """
124          raise NotImplementedError          raise NotImplementedError
125    
126      def _addPadding(self, pad_x, pad_y, NE, l, origin):      def getSurveyData(self, domain, origin, NE, spacing):
127          """          """
128          Helper method that computes new number of elements, length and origin          This method is called by the `DomainBuilder` to retrieve the survey
129          after adding padding to the input values.          data as `Data` objects on the given domain.
130            Subclasses should return one or more `Data` objects with survey data
131          :param pad_x: Number of elements or fraction of padding in x direction          interpolated on the given ripley domain. The exact return type
132          :type pad_x: ``int`` or ``float``          depends on the type of data.
133          :param pad_y: Number of elements or fraction of padding in y direction  
134          :type pad_y: ``int`` or ``float``          :param domain: the escript domain to use
135          :param NE: Initial number of elements          :type domain: `esys.escript.Domain`
136          :type NE: ``tuple`` or ``list``          :param origin: the origin coordinates of the domain
         :param l: Initial side lengths  
         :type l: ``tuple`` or ``list``  
         :param origin: Initial origin  
137          :type origin: ``tuple`` or ``list``          :type origin: ``tuple`` or ``list``
138          :return: tuple with three elements ``(NE_padded, l_padded, origin_padded)``,          :param NE: the number of domain elements in each dimension
139                   which are lists of the updated input parameters          :type NE: ``tuple`` or ``list``
140          """          :param spacing: the cell sizes (node spacing) in the domain
141          DIM=len(NE)          :type spacing: ``tuple`` or ``list``
         frac=[0.]*(DIM-1)+[0]  
         # padding is applied to each side so multiply by 2 to get the total  
         # amount of padding per dimension  
         if pad_x>0 and pad_y<1:  
             frac[0]=2.*pad_x  
         elif pad_x>=1:  
             frac[0]=2.*pad_x/float(NE[0])  
         if DIM>2:  
             if pad_y>0 and pad_y<1:  
                 frac[1]=2.*pad_y  
             elif pad_y>=1:  
                 frac[1]=2.*pad_y/(float(NE[1]))  
   
         # calculate new number of elements  
         NE_new=[int(NE[i]*(1+frac[i])) for i in xrange(DIM)]  
         NEdiff=[NE_new[i]-NE[i] for i in xrange(DIM)]  
         spacing=[l[i]/NE[i] for i in xrange(DIM)]  
         l_new=[NE_new[i]*spacing[i] for i in xrange(DIM)]  
         origin_new=[origin[i]-NEdiff[i]/2.*spacing[i] for i in xrange(DIM)]  
         return NE_new, l_new, origin_new  
   
     def _interpolateOnDomain(self, data):  
         """  
         Helper method that interpolates data arrays onto the domain.  
         Currently this works like a nearest neighbour mapping, i.e. values  
         are directly inserted into data objects at closest location.  
         """  
         dom=self.getDomain()  
         dim=dom.getDim()  
         # determine number of values required per element  
         DPP=Scalar(0., ReducedFunction(dom)).getNumberOfDataPoints()  
         for i in xrange(dim):  
             DPP=DPP/self._dom_NE[i]  
         DPP=int(DPP)  
   
         # idx_mult.dot([x,y,z]) = flat index into data object  
         idx_mult=np.array([DPP]+self._dom_NE[:dim-1]).cumprod()  
   
         # separate data arrays and coordinates  
         num_arrays=len(data[0])-dim  
         arrays=[]  
         for i in xrange(num_arrays):  
             d=Scalar(0., ReducedFunction(dom))  
             d.expand()  
             arrays.append(d)  
   
         for entry in data:  
             index=[int((entry[i]-self._dom_origin[i])/self._spacing[i]) for i in xrange(dim)]  
             index=int(idx_mult.dot(index))  
             for i in xrange(num_arrays):  
                 for p in xrange(DPP):  
                     arrays[i].setValueOfDataPoint(index+p, entry[dim+i])  
   
         return arrays  
   
     def _createDomain(self, padding_x, padding_y):  
         """  
         Creates and returns an escript domain that spans the entire area of  
         available data plus a buffer zone. This method is called only once  
         the first time `getDomain()` is invoked and may be overwritten if  
         required.  
   
         :return: The escript domain for this data source.  
         :rtype: `esys.escript.Domain`  
         """  
         X0, NX, DX = self.getDataExtents()  
         z0, nz, dz = self.getVerticalExtents()  
   
         # number of elements (without padding)  
         NE = [NX[0], NX[1], nz]  
   
         # origin of domain (without padding)  
         origin = [X0[0], X0[1], z0]  
         origin = [np.round(oi) for oi in origin]  
   
         # cell size / point spacing  
         self._spacing = DX+[dz]  
         self._spacing = [float(np.round(si)) for si in self._spacing]  
   
         # length of domain (without padding)  
         l = [NE[i]*self._spacing[i] for i in xrange(len(NE))]  
   
         # now add padding to the values  
         NE_new, l_new, origin_new = self._addPadding(padding_x, padding_y, \  
                 NE, l, origin)  
   
         # number of padding elements per side  
         NE_pad=[(NE_new[i]-NE[i])//2 for i in xrange(3)]  
   
         self._dom_NE_pad = NE_pad  
         self._dom_len = l_new  
         self._dom_NE = NE_new  
         self._dom_origin = origin_new  
         lo=[(origin_new[i], origin_new[i]+l_new[i]) for i in xrange(3)]  
         try:  
             dom=self.getDomainClass()(*self._dom_NE, l0=lo[0], l1=lo[1], l2=lo[2])  
             # ripley may internally adjust NE and length, so recompute  
             self._dom_len=[sup(dom.getX()[i])-inf(dom.getX()[i]) for i in xrange(3)]  
             self._dom_NE=[int(self._dom_len[i]/self._spacing[i]) for i in xrange(3)]  
             x=dom.getX()-[self._dom_origin[i]+NE_pad[i]*self._spacing[i] for i in xrange(3)]  
             mask=wherePositive(dom.getX()[2])  
   
         except TypeError:  
             dom=self.getDomainClass()(*self._dom_NE, l0=l_new[0], l1=l_new[1], l2=l_new[2])  
             x=dom.getX()-[NE_pad[i]*self._spacing[i] for i in xrange(3)]  
             mask=wherePositive(x[2]+self._dom_origin[2])  
   
         # prepare density mask (=1 at padding area, 0 else)  
         if self._constrainSides:  
             for i in xrange(2):  
                 mask=mask + whereNegative(x[i]) + \  
                         wherePositive(x[i]-l_new[i]+2*NE_pad[i]*self._spacing[i])  
   
         if self._constrainBottom:  
             mask = mask + whereNonPositive(x[2])  
         self.setSetDensityMask(wherePositive(mask))  
   
         self.logger.debug("Domain size: %d x %d x %d elements"%(self._dom_NE[0],self._dom_NE[1],self._dom_NE[2]))  
         self.logger.debug("     length: %g x %g x %g"%(self._dom_len[0],self._dom_len[1],self._dom_len[2]))  
         self.logger.debug("     origin: %g x %g x %g"%(origin_new[0],origin_new[1],origin_new[2]))  
   
         return dom  
   
   
 ##############################################################################  
 class UBCDataSource(DataSource):  
     def __init__(self, domainclass, meshfile, gravfile, topofile=None):  
         super(UBCDataSource,self).__init__()  
         self.__meshfile=meshfile  
         self.__gravfile=gravfile  
         self.__topofile=topofile  
         self.__domainclass=domainclass  
         self.__readMesh()  
   
     def __readMesh(self):  
         meshdata=open(self.__meshfile).readlines()  
         numDataPoints=meshdata[0].split()  
         origin=meshdata[1].split()  
         self.__nPts=map(int, numDataPoints)  
         self.__origin=map(float, origin)  
         self.__delta=[float(X.split('*')[1]) for X in meshdata[2:]]  
         # vertical data is upside down  
         self.__origin[2]-=(self.__nPts[2]-1)*self.__delta[2]  
         self.logger.debug("Data Source: %s (mesh file: %s)"%(self.__gravfile, self.__meshfile))  
   
     def getDataExtents(self):  
         """  
         returns ( (x0, y0), (nx, ny), (dx, dy) )  
         """  
         return (self.__origin[:2], self.__nPts[:2], self.__delta[:2])  
   
     def getVerticalExtents(self):  
         """  
         returns (z0, nz, dz)  
         """  
         return (self.__origin[2], self.__nPts[2], self.__delta[2])  
   
     def getDomainClass(self):  
         """  
         returns the domain generator class (e.g. esys.ripley.Brick)  
         """  
         return self.__domainclass  
   
     #def getSetDensityMask(self):  
     #    topodata=self.__readTopography()  
     #    mask=self._interpolateOnDomain(topodata)  
     #    mask=wherePositive(self.getDomain().getX()[2]-mask[0])  
     #    return mask  
   
     def getGravityAndStdDev(self):  
         gravlist=self.__readGravity() # x,y,z,g,s  
         g_and_sigma=self._interpolateOnDomain(gravlist)  
         return g_and_sigma[0]*[0,0,1], g_and_sigma[1]  
   
     def __readTopography(self):  
         f=open(self.__topofile)  
         n=int(f.readline())  
         topodata=np.zeros((n,3))  
         for i in xrange(n):  
             x=f.readline().split()  
             x=map(float, x)  
             topodata[i]=x  
         f.close()  
         return topodata  
   
     def __readGravity(self):  
         f=open(self.__gravfile)  
         n=int(f.readline())  
         gravdata=np.zeros((n,5))  
         for i in xrange(n):  
             x=f.readline().split()  
             x=map(float, x) # x, y, z, anomaly in mGal, stddev  
             # convert gravity anomaly units to m/s^2 and rescale error  
             x[3]*=-1e-5  
             x[4]*=1e-5  
             gravdata[i]=x  
         f.close()  
         return gravdata  
   
 ##############################################################################  
 class NetCDFDataSource(DataSource):  
     def __init__(self, gravfile=None, magfile=None, topofile=None, vertical_extents=(-40000,10000,25), alt_of_data=0.):  
         """  
         vertical_extents - (alt_min, alt_max, num_points)  
         alt_of_data - altitude of measurements  
         """  
         super(NetCDFDataSource,self).__init__()  
         self.__topofile=topofile  
         self.__gravfile=gravfile  
         self.__magfile=magfile  
         self.__determineExtents(vertical_extents)  
         self.__altOfData=alt_of_data  
   
     def __determineExtents(self, ve):  
         self.logger.debug("Data Source: %s"%self.__gravfile)  
         f=netcdf_file(self.__gravfile, 'r')  
         NX=0  
         for n in ['lon','longitude','x']:  
             if n in f.dimensions:  
                 NX=f.dimensions[n]  
                 break  
         if NX==0:  
             raise RuntimeError("Could not determine extents of data")  
         NY=0  
         for n in ['lat','latitude','y']:  
             if n in f.dimensions:  
                 NY=f.dimensions[n]  
                 break  
         if NY==0:  
             raise RuntimeError("Could not determine extents of data")  
   
         # find longitude and latitude variables  
         lon_name=None  
         for n in ['lon','longitude']:  
             if n in f.variables:  
                 lon_name=n  
                 longitude=f.variables.pop(n)  
                 break  
         if lon_name is None:  
             raise RuntimeError("Could not determine longitude variable")  
         lat_name=None  
         for n in ['lat','latitude']:  
             if n in f.variables:  
                 lat_name=n  
                 latitude=f.variables.pop(n)  
                 break  
         if lat_name is None:  
             raise RuntimeError("Could not determine latitude variable")  
   
         # try to figure out gravity variable name  
         grav_name=None  
         if len(f.variables)==1:  
             grav_name=f.variables.keys()[0]  
         else:  
             for n in f.variables.keys():  
                 dims=f.variables[n].dimensions  
                 if (lat_name in dims) and (lon_name in dims):  
                     grav_name=n  
                     break  
         if grav_name is None:  
             raise RuntimeError("Could not determine gravity variable")  
   
         # try to determine value for unused data  
         if hasattr(f.variables[grav_name], 'missing_value'):  
             maskval = float(f.variables[grav_name].missing_value)  
         elif hasattr(f.variables[grav_name], '_FillValue'):  
             maskval = float(f.variables[grav_name]._FillValue)  
         else:  
             self.logger.debug("missing_value attribute not found, using default.")  
             maskval = 99999  
   
         # see if there is a wkt string to convert coordinates  
         try:  
             wkt_string=f.variables[grav_name].esri_pe_string  
         except:  
             wkt_string=None  
   
         # we don't trust actual_range & geospatial_lon_min/max since subset  
         # data does not seem to have these fields updated.  
         # Getting min/max from the arrays is obviously not very efficient but..  
         #lon_range=longitude.actual_range  
         #lat_range=latitude.actual_range  
         #lon_range=[f.geospatial_lon_min,f.geospatial_lon_max]  
         #lat_range=[f.geospatial_lat_min,f.geospatial_lat_max]  
         lon_range=longitude.data.min(),longitude.data.max()  
         lat_range=latitude.data.min(),latitude.data.max()  
         lon_range,lat_range=LatLonToUTM(lon_range, lat_range, wkt_string)  
         origin=[lon_range[0],lat_range[0],ve[0]]  
         lengths=[lon_range[1]-lon_range[0], lat_range[1]-lat_range[0],ve[1]-ve[0]]  
   
         f.close()  
   
         self.__nPts=[NX, NY, ve[2]]  
         self.__origin=origin  
         # we are rounding to avoid interpolation issues  
         self.__delta=[np.round(lengths[i]/self.__nPts[i]) for i in xrange(3)]  
         self.__wkt_string=wkt_string  
         self.__lon=lon_name  
         self.__lat=lat_name  
         self.__grv=grav_name  
         self.__maskval=maskval  
   
     def getDataExtents(self):  
         """  
         returns ( (x0, y0), (nx, ny), (dx, dy) )  
142          """          """
143          return (self.__origin[:2], self.__nPts[:2], self.__delta[:2])          raise NotImplementedError
144    
145      def getVerticalExtents(self):      def setSubsamplingFactor(self, f):
146          """          """
147          returns (z0, nz, dz)          Sets the data subsampling factor (default=1).
148            The factor is applied in all dimensions. For example a 2D dataset
149            with 300 x 150 data points will be reduced to 150 x 75 when a
150            subsampling factor of 2 is used.
151            This becomes important when adding data of varying resolution to
152            a `DomainBuilder`.
153          """          """
154          return (self.__origin[2], self.__nPts[2], self.__delta[2])          self.__subsampling_factor=f
155    
156      def getDomainClass(self):      def getSubsamplingFactor(self):
157          """          """
158          returns the domain generator class (e.g. esys.ripley.Brick)          Returns the subsampling factor that was set via `setSubsamplingFactor`
159            (see there).
160          """          """
161          return Brick          return self.__subsampling_factor
162    
     def getGravityAndStdDev(self):  
         nValues=self.__nPts[:2]+[1]  
         first=self._dom_NE_pad[:2]+[self._dom_NE_pad[2]+int((self.__altOfData-self.__origin[2])/self.__delta[2])]  
         g=ripleycpp._readNcGrid(self.__gravfile, self.__grv,  
                 ReducedFunction(self.getDomain()),  
                 first, nValues, (), self.__maskval)  
         sigma=whereNonZero(g-self.__maskval)  
         g=g*1e-6  
         sigma=sigma*2e-6  
         return g*[0,0,1], sigma  
   
     def _readTopography(self):  
         f=netcdf_file(self.__topofile, 'r')  
         lon=None  
         for n in ['lon','longitude']:  
             if n in f.variables:  
                 lon=f.variables[n][:]  
                 break  
         if lon is None:  
             raise RuntimeError("Could not determine longitude variable")  
         lat=None  
         for n in ['lat','latitude']:  
             if n in f.variables:  
                 lat=f.variables[n][:]  
                 break  
         if lat is None:  
             raise RuntimeError("Could not determine latitude variable")  
         alt=None  
         for n in ['altitude','alt']:  
             if n in f.variables:  
                 alt=f.variables[n][:]  
                 break  
         if alt is None:  
             raise RuntimeError("Could not determine altitude variable")  
   
         topodata=np.column_stack((lon,lat,alt))  
         f.close()  
         return topodata  
163    
164  ##############################################################################  ##############################################################################
165  class ERSDataSource(DataSource):  class ErMapperData(DataSource):
166      """      """
167      Data Source for ER Mapper raster data.      Data Source for ER Mapper raster data.
168      Note that this class only accepts a very specific type of ER Mapper data      Note that this class only accepts a very specific type of ER Mapper data
169      input and will raise an exception if other data is found.      input and will raise an exception if other data is found.
170      """      """
171      def __init__(self, headerfile, datafile=None, vertical_extents=(-40000,10000,25), alt_of_data=0.):      def __init__(self, datatype, headerfile, datafile=None, altitude=0.):
172          """          """
173          headerfile - usually ends in .ers          :param datatype: type of data, must be `GRAVITY` or `MAGNETIC`
174          datafile - usually has the same name as the headerfile without '.ers'          :type datatype: ``int``
175            :param headerfile: ER Mapper header file (usually ends in .ers)
176            :type headerfile: ``str``
177            :param datafile: ER Mapper binary data file name. If not supplied the
178                             name of the header file without '.ers' is assumed
179            :type datafile: ``str``
180            :param altitude: altitude of measurements above ground in meters
181            :type altitude: ``float``
182          """          """
183          super(ERSDataSource,self).__init__()          super(ErMapperData,self).__init__()
184          self.__headerfile=headerfile          self.__headerfile=headerfile
185          if datafile is None:          if datafile is None:
186              self.__datafile=headerfile[:-4]              self.__datafile=headerfile[:-4]
187          else:          else:
188              self.__datafile=datafile              self.__datafile=datafile
189          self.__readHeader(vertical_extents)          self.__altitude=altitude
190          self.__altOfData=alt_of_data          self.__datatype=datatype
191            self.__readHeader()
192    
193      def __readHeader(self, ve):      def __readHeader(self):
194          self.logger.debug("Data Source: %s (header: %s)"%(self.__datafile, self.__headerfile))          self.logger.debug("Checking Data Source: %s (header: %s)"%(self.__datafile, self.__headerfile))
195          metadata=open(self.__headerfile, 'r').readlines()          metadata=open(self.__headerfile, 'r').readlines()
196          # parse metadata          # parse metadata
197          start=-1          start=-1
198          for i in xrange(len(metadata)):          for i in range(len(metadata)):
199              if metadata[i].strip() == 'DatasetHeader Begin':              if metadata[i].strip() == 'DatasetHeader Begin':
200                  start=i+1                  start=i+1
201          if start==-1:          if start==-1:
# Line 645  class ERSDataSource(DataSource): Line 203  class ERSDataSource(DataSource):
203    
204          md_dict={}          md_dict={}
205          section=[]          section=[]
206          for i in xrange(start, len(metadata)):          for i in range(start, len(metadata)):
207              line=metadata[i]              line=metadata[i]
208              if line[-6:].strip() == 'Begin':              if line[-6:].strip() == 'Begin':
209                  section.append(line[:-6].strip())                  section.append(line[:-6].strip())
# Line 717  class ERSDataSource(DataSource): Line 275  class ERSDataSource(DataSource):
275          # data sets have origin in top-left corner so y runs top-down          # data sets have origin in top-left corner so y runs top-down
276          self.__dataorigin=[originX, originY]          self.__dataorigin=[originX, originY]
277          originY-=(NY-1)*spacingY          originY-=(NY-1)*spacingY
278          self.__maskval=maskval          self.__delta = [spacingX, spacingY]
279          spacingZ=np.round(float(ve[1]-ve[0])/ve[2])          self.__maskval = maskval
280          self.__delta = [spacingX, spacingY, spacingZ]          self.__nPts = [NX, NY]
281          self.__nPts = [NX, NY, ve[2]]          self.__origin = [originX, originY]
282          self.__origin = [originX, originY, ve[0]]          if self.__datatype == self.GRAVITY:
283                self.logger.info("Assuming gravity data scale is 1e-6 m/s^2.")
284                self.__scalefactor = 1e-6
285            else:
286                self.logger.info("Assuming magnetic data units are 'nT'.")
287                self.__scalefactor = 1e-9
288    
289      def getDataExtents(self):      def getDataExtents(self):
290          """          """
291          returns ( (x0, y0), (nx, ny), (dx, dy) )          returns ( (x0, y0), (nx, ny), (dx, dy) )
292          """          """
293          return (self.__origin[:2], self.__nPts[:2], self.__delta[:2])          return (list(self.__origin), list(self.__nPts), list(self.__delta))
294    
295      def getVerticalExtents(self):      def getDataType(self):
296          """          return self.__datatype
297          returns (z0, nz, dz)  
298        def getSurveyData(self, domain, origin, NE, spacing):
299            nValues=self.__nPts
300            # determine base location of this dataset within the domain
301            first=[int((self.__origin[i]-origin[i])/spacing[i]) for i in range(len(self.__nPts))]
302            if domain.getDim()==3:
303                first.append(int((self.__altitude-origin[2])/spacing[2]))
304                nValues=nValues+[1]
305    
306            data=ripleycpp._readBinaryGrid(self.__datafile,
307                    ReducedFunction(domain),
308                    first, nValues, (), self.__maskval)
309            sigma = whereNonZero(data-self.__maskval)
310            data = data*self.__scalefactor
311            sigma = sigma * 2. * self.__scalefactor
312            return data, sigma
313    
314    
315    ##############################################################################
316    class NetCdfData(DataSource):
317        """
318        Data Source for gridded netCDF data that use CF/COARDS conventions.
319        """
320        def __init__(self, datatype, filename, altitude=0.):
321          """          """
322          return (self.__origin[2], self.__nPts[2], self.__delta[2])          :param filename: file name for survey data in netCDF format
323            :type filename: ``str``
324            :param datatype: type of data, must be `GRAVITY` or `MAGNETIC`
325            :type datatype: ``int``
326            :param altitude: altitude of measurements in meters
327            :type altitude: ``float``
328            """
329            super(NetCdfData,self).__init__()
330            self.__filename=filename
331            if not datatype in [self.GRAVITY,self.MAGNETIC]:
332                raise ValueError("Invalid value for datatype parameter")
333            self.__datatype=datatype
334            self.__altitude=altitude
335            self.__readMetadata()
336    
337        def __readMetadata(self):
338            self.logger.debug("Checking Data Source: %s"%self.__filename)
339            f=netcdf_file(self.__filename, 'r')
340            NX=0
341            for n in ['lon','longitude','x']:
342                if n in f.dimensions:
343                    NX=f.dimensions[n]
344                    break
345            if NX==0:
346                raise RuntimeError("Could not determine extents of data")
347            NY=0
348            for n in ['lat','latitude','y']:
349                if n in f.dimensions:
350                    NY=f.dimensions[n]
351                    break
352            if NY==0:
353                raise RuntimeError("Could not determine extents of data")
354    
355      def getDomainClass(self):          # find longitude and latitude variables
356            lon_name=None
357            for n in ['lon','longitude']:
358                if n in f.variables:
359                    lon_name=n
360                    longitude=f.variables.pop(n)
361                    break
362            if lon_name is None:
363                raise RuntimeError("Could not determine longitude variable")
364            lat_name=None
365            for n in ['lat','latitude']:
366                if n in f.variables:
367                    lat_name=n
368                    latitude=f.variables.pop(n)
369                    break
370            if lat_name is None:
371                raise RuntimeError("Could not determine latitude variable")
372    
373            # try to figure out data variable name
374            data_name=None
375            if len(f.variables)==1:
376                data_name=f.variables.keys()[0]
377            else:
378                for n in f.variables.keys():
379                    dims=f.variables[n].dimensions
380                    if (lat_name in dims) and (lon_name in dims):
381                        data_name=n
382                        break
383            if data_name is None:
384                raise RuntimeError("Could not determine data variable")
385    
386            # try to determine value for unused data
387            if hasattr(f.variables[data_name], 'missing_value'):
388                maskval = float(f.variables[data_name].missing_value)
389            elif hasattr(f.variables[data_name], '_FillValue'):
390                maskval = float(f.variables[data_name]._FillValue)
391            else:
392                self.logger.debug("missing_value attribute not found, using default.")
393                maskval = 99999
394    
395            # try to determine units of data - this is disabled for now
396            #if hasattr(f.variables[data_name], 'units'):
397            #   units=f.variables[data_name].units
398            if self.__datatype == self.GRAVITY:
399                self.logger.info("Assuming gravity data scale is 1e-6 m/s^2.")
400                self.__scalefactor = 1e-6
401            else:
402                self.logger.info("Assuming magnetic data units are 'nT'.")
403                self.__scalefactor = 1e-9
404    
405            # see if there is a wkt string to convert coordinates
406            try:
407                wkt_string=f.variables[data_name].esri_pe_string
408            except:
409                wkt_string=None
410    
411            # we don't trust actual_range & geospatial_lon_min/max since subset
412            # data does not seem to have these fields updated.
413            # Getting min/max from the arrays is obviously not very efficient but..
414            #lon_range=longitude.actual_range
415            #lat_range=latitude.actual_range
416            #lon_range=[f.geospatial_lon_min,f.geospatial_lon_max]
417            #lat_range=[f.geospatial_lat_min,f.geospatial_lat_max]
418            lon_range=longitude.data.min(),longitude.data.max()
419            lat_range=latitude.data.min(),latitude.data.max()
420            if lon_range[1]<180:
421                lon_range,lat_range=LatLonToUTM(lon_range, lat_range, wkt_string)
422            lengths=[lon_range[1]-lon_range[0], lat_range[1]-lat_range[0]]
423            f.close()
424    
425            self.__nPts=[NX, NY]
426            self.__origin=[lon_range[0],lat_range[0]]
427            # we are rounding to avoid interpolation issues
428            self.__delta=[np.round(lengths[i]/self.__nPts[i]) for i in range(2)]
429            #self.__wkt_string=wkt_string
430            #self.__lon_name=lon_name
431            #self.__lat_name=lat_name
432            self.__data_name=data_name
433            self.__maskval=maskval
434    
435        def getDataExtents(self):
436          """          """
437          returns the domain generator class (e.g. esys.ripley.Brick)          returns ( (x0, y0), (nx, ny), (dx, dy) )
438          """          """
439          return Brick          return (list(self.__origin), list(self.__nPts), list(self.__delta))
440    
441      def getGravityAndStdDev(self):      def getDataType(self):
442          nValues=self.__nPts[:2]+[1]          return self.__datatype
443          first=self._dom_NE_pad[:2]+[self._dom_NE_pad[2]+int((self.__altOfData-self.__origin[2])/self.__delta[2])]  
444          g=ripleycpp._readBinaryGrid(self.__datafile,      def getSurveyData(self, domain, origin, NE, spacing):
445                  ReducedFunction(self.getDomain()),          nValues=self.__nPts
446                  first, nValues, (), self.__maskval)          # determine base location of this dataset within the domain
447          sigma=whereNonZero(g-self.__maskval)          first=[int((self.__origin[i]-origin[i])/spacing[i]) for i in range(len(self.__nPts))]
448          g=g*1e-6          if domain.getDim()==3:
449          sigma=sigma*2e-6              first.append(int((self.__altitude-origin[2])/spacing[2]))
450          return g*[0,0,1], sigma              nValues=nValues+[1]
451    
452            data=ripleycpp._readNcGrid(self.__filename, self.__data_name,
453                      ReducedFunction(domain), first, nValues, (), self.__maskval)
454            sigma=whereNonZero(data-self.__maskval)
455            data=data*self.__scalefactor
456            sigma=sigma * 2. * self.__scalefactor
457            return data, sigma
458    
459    
460  ##############################################################################  ##############################################################################
# Line 760  class SourceFeature(object): Line 464  class SourceFeature(object):
464      data source, for example a layer of a specific rock type or a simulated      data source, for example a layer of a specific rock type or a simulated
465      ore body.      ore body.
466      """      """
467      def getDensity(self):      def getValue(self):
468          """          """
469          Returns the density for the area covered by mask. It can be constant          Returns the value for the area covered by mask. It can be constant
470          or a data object with spatial dependency.          or a data object with spatial dependency.
471          """          """
472          raise NotImplementedError          raise NotImplementedError
# Line 775  class SourceFeature(object): Line 479  class SourceFeature(object):
479          raise NotImplementedError          raise NotImplementedError
480    
481  class SmoothAnomaly(SourceFeature):  class SmoothAnomaly(SourceFeature):
482      def __init__(self, lx, ly, lz, x, y, depth, rho_inner=None, rho_outer=None, k_inner=None, k_outer=None):      def __init__(self, lx, ly, lz, x, y, depth, v_inner=None, v_outer=None):
483          self.x=x          self.x=x
484          self.y=y          self.y=y
485          self.lx=lx          self.lx=lx
486          self.ly=ly          self.ly=ly
487          self.lz=lz          self.lz=lz
488          self.depth=depth          self.depth=depth
489          self.rho_inner=rho_inner          self.v_inner=v_inner
490          self.rho_outer=rho_outer          self.v_outer=v_outer
491          self.k_inner=k_inner          self.value=None
492          self.k_outer=k_outer          self.mask=None
493          self.rho=None  
494          self.k=None      def getValue(self,x):
495          self.mask=None              if self.value is None:
496                if self.v_outer is None or self.v_inner is None:
497      def getDensity(self,x):                  self.value=0
         if self.rho is None:  
             if self.rho_outer is None or self.rho_inner is None:  
                 self.rho=0  
             else:  
                 DIM=x.getDomain().getDim()    
                 alpha=-log(abs(self.rho_outer/self.rho_inner))*4  
                 rho=exp(-alpha*((x[0]-self.x)/self.lx)**2)  
                 rho=rho*exp(-alpha*((x[DIM-1]-(sup(x[DIM-1])-self.depth))/self.lz)**2)  
                 self.rho=maximum(abs(self.rho_outer), abs(self.rho_inner*rho))  
                 if self.rho_inner<0: self.rho=-self.rho  
               
         return self.rho  
           
     def getSusceptibility(self,x):  
          if self.k is None:  
             if self.k_outer is None or self.k_inner is None:  
                 self.k=0  
498              else:              else:
499                  DIM=x.getDomain().getDim()                    DIM=x.getDomain().getDim()
500                  alpha=-log(abs(self.k_outer/self.k_inner))*4                  alpha=-log(abs(self.v_outer/self.v_inner))*4
501                  k=exp(-alpha*((x[0]-self.x)/self.lx)**2)                  value=exp(-alpha*((x[0]-self.x)/self.lx)**2)
502                  k=k*exp(-alpha*((x[DIM-1]-(sup(x[DIM-1])-self.depth))/self.lz)**2)                  value=value*exp(-alpha*((x[DIM-1]+self.depth)/self.lz)**2)
503                  self.k=maximum(abs(self.k_outer), abs(self.k_inner*k))                  self.value=maximum(abs(self.v_outer), abs(self.v_inner*value))
504                  if self.k_inner<0: self.k=-self.k                  if self.v_inner<0: self.value=-self.value
505                
506           return self.k          return self.value
507            
508      def getMask(self, x):      def getMask(self, x):
509          DIM=x.getDomain().getDim()          DIM=x.getDomain().getDim()
510          m=whereNonNegative(x[DIM-1]-(sup(x[DIM-1])-self.depth-self.lz/2)) * whereNonPositive(x[DIM-1]-(sup(x[DIM-1])-self.depth+self.lz/2)) \          m=whereNonNegative(x[DIM-1]+self.depth+self.lz/2) * whereNonPositive(x[DIM-1]+self.depth-self.lz/2) \
511              *whereNonNegative(x[0]-(self.x-self.lx/2)) * whereNonPositive(x[0]-(self.x+self.lx/2))              *whereNonNegative(x[0]-(self.x-self.lx/2)) * whereNonPositive(x[0]-(self.x+self.lx/2))
512          if DIM>2:          if DIM>2:
513              m*=whereNonNegative(x[1]-(self.y-self.ly/2)) * whereNonPositive(x[1]-(self.y+self.ly/2))              m*=whereNonNegative(x[1]-(self.y-self.ly/2)) * whereNonPositive(x[1]-(self.y+self.ly/2))
514          self.mask = m                  self.mask = m
515          return m          return m
516    
517  ##############################################################################  ##############################################################################
518  class SyntheticDataSource(DataSource):  class SyntheticDataBase(DataSource):
519      def __init__(self, DIM, NE, l, h, features, latitude=-32.):    """
520          super(SyntheticDataSource,self).__init__()    Base class to define reference data based on a given property distribution (density or
521          self._features = features    susceptibility). Data are collected from a square region of vertical extend `length` on a
522          self.DIM=DIM    grid with ``number_of_elements`` cells in each direction.
523          self.NE=NE    
524          self.l=l    The synthetic data are constructed by solving the appropriate forward problem. Data can be sampled
525          self.h=h    with an offset from the surface at z=0 or using the entire subsurface region.
526          self.latitude=latitude    """
527      def __init__(self, datatype,
528      def _createDomain(self, padding_x, padding_y):          DIM=2,
529          NE_H=self.NE          number_of_elements=10,
530          NE_L=int((self.l/self.h)*NE_H+0.5)          length=1*U.km,
531          l=[self.l]*(self.DIM-1)+[self.h]          B_b=None,
532          NE=[NE_L]*(self.DIM-1)+[NE_H]          data_offset=0,
533          origin=[0.]*self.DIM          full_knowledge=False,
534          NE_new, l_new, origin_new = self._addPadding(padding_x, padding_y, \          spherical=False):
535                  NE, l, origin)      """
536            :param datatype: data type indicator
537          self.NE=NE_new          :type datatype: ``DataSource.GRAVITY``, ``DataSource.MAGNETIC``
538          self.l=l_new[0]          :param DIM: spatial dimension
539          self.h=l_new[self.DIM-1]          :type DIM: 2 or 3
540            :param number_of_elements: lateral number of elements in the region where data are collected
541          self.logger.debug("Data Source: synthetic with %d features"%len(self._features))          :type number_of_elements: ``int``
542          if self.DIM==2:          :param length: lateral extend of the region where data are collected
543              from esys.finley import Rectangle          :type length: ``float``
544              dom = Rectangle(n0=NE_new[0], n1=NE_new[1], l0=l_new[0], l1=l_new[1])          :param B_b: background magnetic flux density [B_r, B_latiude, B_longitude]. Only used for magnetic data.
545              self._x = dom.getX() + origin_new          :type B_b: ``list`` of ``Scalar``
546              self.logger.debug("Domain size: %d x %d elements"%(NE_new[0], NE_new[1]))          :param data_offset: offset of the data collection region from the surface
547              self.logger.debug("     length: %g x %g"%(l_new[0],l_new[1]))          :type data_offset: ``float``
548              self.logger.debug("     origin: %g x %g"%(origin_new[0],origin_new[1]))          :param full_knowledge: if ``True`` data are collected from the entire subsurface region. This is mainly for testing.
549          else:          :type full_knowledge: ``Bool``
550              from esys.finley import Brick          :param spherical: if ``True`` sperical coordinates are used (ignored)
551              dom = Brick(n0=NE_new[0], n1=NE_new[1], n2=NE_new[2], l0=l_new[0], l1=l_new[1], l2=l_new[2])          :type spherical: ``Bool``
552              self._x = dom.getX() + origin_new      """
553              self.logger.debug("Domain size: %d x %d x %d elements"%(self.NE[0],self.NE[1],self.NE[2]))      super(SyntheticDataBase,self).__init__()
554              self.logger.debug("     length: %g x %g x %g"%(l_new[0],l_new[1],l_new[2]))      if not datatype in [self.GRAVITY,self.MAGNETIC]:
555              self.logger.debug("     origin: %g x %g x %g"%(origin_new[0],origin_new[1],origin_new[2]))          raise ValueError("Invalid value for datatype parameter")      
556        self.DIM=DIM
557          dz=l_new[self.DIM-1]/NE_new[self.DIM-1]      self.number_of_elements=number_of_elements
558          self._g_mask=wherePositive(dom.getX()[0]-origin_new[0]) \      self.length=length
559                  * whereNegative(dom.getX()[0]-(l_new[0]-origin_new[0])) \      self.__datatype = datatype
560                  * whereNonNegative(dom.getX()[self.DIM-1]-(l_new[self.DIM-1]+origin_new[self.DIM-1])) \      
561                  * whereNonPositive(dom.getX()[self.DIM-1]-(l_new[self.DIM-1]+(origin_new[self.DIM-1]+dz)))      self.__spherical = spherical  
562                        self.__full_knowledge= full_knowledge
563          self._B_mask=self._g_mask      self.__data_offset=data_offset
564                self.__B_b =None
565          mask=whereNegative(self._x[self.DIM-1]) + \      # this is for Cartesian (FIXME ?)
566                  wherePositive(self._x[self.DIM-1]-l[self.DIM-1])      if datatype  ==  self.MAGNETIC:
567          for i in xrange(self.DIM-1):          if self.DIM<3:
568              mask+= whereNegative(self._x[i]) +  wherePositive(self._x[i]-l[i])            self.__B_b =  np.array([-B_b[2],  -B_b[0]])
569          self.setSetDensityMask(wherePositive(mask))          else:
570          self.setSetSusceptibilityMask(wherePositive(mask))            self.__B_b = ([-B_b[1],  -B_b[2],  -B_b[0]])    
571        self.__origin = [0]*(DIM-1)
572          rho_ref=0.      self.__delta = [float(length)/number_of_elements]*(DIM-1)
573          k_ref=0      self.__nPts = [number_of_elements]*(DIM-1)
574          for f in self._features:      self._reference_data=None
575              m=f.getMask(self._x)      
576              rho_ref = rho_ref * (1-m) + f.getDensity(self._x) * m    def getDataExtents(self):
577              k_ref = k_ref * (1-m) + f.getSusceptibility(self._x) * m      """
578          self._rho=rho_ref      returns the lateral data extend of the data set
579          self._k=k_ref      """
580        return (list(self.__origin), list(self.__nPts), list(self.__delta))
581          return dom  
582      def getDataType(self):
583        """
584      def getReferenceDensity(self):      returns the data type
585          return self._rho      """
586      def getReferenceSusceptibility(self):      return self.__datatype
587          return self._k      
588      def getSurveyData(self, domain, origin, number_of_elements, spacing):
589      def getGravityAndStdDev(self):      """
590          pde=LinearSinglePDE(self.getDomain())      returns the survey data placed on a given domain.
591          G=U.Gravitational_Constant      
592          m_psi_ref=0.      :param domain: domain on which the data are to be placed
593          for i in xrange(self.DIM):      :type param: ``Domain``
594              m_psi_ref=m_psi_ref + whereZero(self._x[i]-inf(self._x[i])) \      :param origin: origin of the domain
595                      + whereZero(self._x[i]-sup(self._x[i]))      :type origin: ``list`` of ``float``
596        :param number_of_elements: number of elements (or cells) in each spatial direction used
597          pde.setValue(A=kronecker(self.getDomain()), Y=-4*np.pi*G*self._rho, q=m_psi_ref)                    span the domain
598          pde.setSymmetryOn()      :type number_of_elements: `list`` of ``int``
599          psi_ref=pde.getSolution()      :param spacing: cell size in each spatial direction
600          del pde      :type spacing: ``list`` of ``float``
601          g=-grad(psi_ref)      :return: observed gravity field or magnetic flux density for each cell in the domain and
602          sigma=self._g_mask      for each cell an indicator 1/0 if the data are valid or not.
603          return g,sigma      :rtype: pair of ``Scalar``
604                """
605      def getMagneticFlieldAndStdDev(self):      pde=LinearSinglePDE(domain)
606        DIM=domain.getDim()
607        x=domain.getX()
608        # set the reference data
609        
610        k=self.getReferenceProperty(domain)
611        # calculate the corresponding potential
612        z=x[DIM-1]
613        m_psi_ref=whereZero(z-sup(z))
614        if self.getDataType()==DataSource.GRAVITY:
615            pde.setValue(A=kronecker(domain), Y=-4*np.pi*U.Gravitational_Constant*self._reference_data, q=m_psi_ref)
616        else:
617            pde.setValue(A=kronecker(domain), X=self._reference_data*self.__B_b, q=m_psi_ref)
618        pde.setSymmetryOn()
619        psi_ref=pde.getSolution()
620        del pde
621        if self.getDataType()==DataSource.GRAVITY:
622            data = -grad(psi_ref, ReducedFunction(domain))
623        else:
624            data = self._reference_data*self.__B_b-grad(psi_ref, ReducedFunction(domain))
625          
626        x=ReducedFunction(domain).getX()    
627        if self.__full_knowledge:
628            sigma = whereNegative(x[DIM-1])
629        else:
630          
631            sigma=1.
632            # limit mask to non-padding in horizontal area        
633            for i in range(DIM-1):
634            x_i=x[i]
635            sigma=sigma * wherePositive(x_i) * whereNegative(x_i-(sup(x_i)+inf(x_i)))
636            # limit mask to one cell thickness at z=0
637            z=x[DIM-1]
638            oo=int(self.__data_offset/spacing[DIM-1]+0.5)*spacing[DIM-1]
639            sigma = sigma * whereNonNegative(z-oo) * whereNonPositive(z-oo-spacing[DIM-1])
640        return data,sigma
641        
642      def getReferenceProperty(self, domain=None):
643        """
644        Returns the reference density Data object that was used to generate
645        the gravity/susceptibility anomaly data.
646        
647        :return: the density or susceptibility anomaly used to create the survey data.
648        :note: it can be assumed that in the first call the ``domain`` argument is present so the
649        actual anomaly data can be created. In subsequent calls this may not be true.
650        :note: method needs to be overwritten
651        """
652        raise NotImplementedError()      
653                
654          pde=LinearSinglePDE(self.getDomain())  class SyntheticFeatureData(SyntheticDataBase):
655          B_b=self.getBackgroundMagneticField()      """
656          DIM=self.getDomain().getDim()      uses a list of ``SourceFeature`` to define synthetic anomaly data
657          m_psi_ref=0.      """
658          for i in xrange(self.DIM):      def __init__(self, datatype,
659              m_psi_ref=m_psi_ref + whereZero(self._x[i]-inf(self._x[i])) \                         features,
660                      + whereZero(self._x[i]-sup(self._x[i]))                                 DIM=2,
661          pde.setValue(A=kronecker(self.getDomain()), X=(1+self._k)*B_b, q=m_psi_ref)                         number_of_elements=10,
662          pde.setSymmetryOn()                         length=1*U.km,
663          psi_ref=pde.getSolution()                         B_b=None,
664          del pde                         data_offset=0,
665          B= (1+self._k) * B_b -grad(psi_ref)                         full_knowledge=False,
666          sigma=self._B_mask                         spherical=False):
667          return B,sigma          """
668            :param datatype: data type indicator
669            :type datatype: ``DataSource.GRAVITY``, ``DataSource.MAGNETIC``
670            :param features: list of features. It is recommended that the features do entirely lay below surface.
671            :type features: ``list`` of ``SourceFeature``
672            :param DIM: spatial dimension
673            :type DIM: 2 or 3
674            :param number_of_elements: lateral number of elements in the region where data are collected
675            :type number_of_elements: ``int``
676            :param length: lateral extend of the region where data are collected
677            :type length: ``float``
678            :param B_b: background magnetic flux density [B_r, B_latiude, B_longitude]. Only used for magnetic data.
679            :type B_b: ``list`` of ``Scalar``
680            :param data_offset: offset of the data collection region from the surface
681            :type data_offset: ``float``
682            :param full_knowledge: if ``True`` data are collected from the entire subsurface region. This is mainly for testing.
683            :type full_knowledge: ``Bool``
684            :param spherical: if ``True`` sperical coordinates are used (ignored)
685            :type spherical: ``Bool``
686            """
687            super(SyntheticFeatureData,self).__init__(
688                                     datatype=datatype, DIM=DIM, number_of_elements=number_of_elements,
689                                     length=length, B_b=B_b,
690                                     data_offset=data_offset,
691                                     full_knowledge=full_knowledge,
692                                     spherical=spherical)
693            self._features = features
694    
695    
696        def getReferenceProperty(self, domain=None):
697            """
698        Returns the reference density Data object that was used to generate
699        the gravity/susceptibility anomaly data.
700            """
701            if self._reference_data == None:
702                DIM=domain.getDim()
703                x=domain.getX()
704                k=0.
705                for f in self._features:
706                    m=f.getMask(x)
707                    k = k * (1-m) + f.getValue(x) * m
708                self._reference_data= k
709            return self._reference_data
710                    
711    class SyntheticData(SyntheticDataBase):
712        """
713        defines synthetic  gravity/magnetic data based on harmonic property anomaly
714        
715            rho = mean + amplitude * sin(n_depth * pi /depth * z) * sin(n_length * pi /length * x - shift )
716                    
717      def getBackgroundMagneticField(self):      for all x and z<=0. for z>0 rho = 0.        
718         theta = (90-self.latitude)/180.*np.pi      """
719         B_0=U.Mu_0  * U.Magnetic_Dipole_Moment_Earth / (4 * np.pi *  U.R_Earth**3)      def __init__(self, datatype,
720         B_theta= B_0 * sin(theta)                         n_length=1,
721         B_r= 2 * B_0 * cos(theta)                         n_depth=1,
722         DIM=self.getDomain().getDim()                         shift=0.,
723         if DIM<3:                         amplitude=None,
724            return np.array([0.,  -B_r])                         DIM=2,
725         else:                         number_of_elements=10,
726            return np.array([-B_theta, 0.,  -B_r])                         length=1*U.km,
727                           B_b=None,
728                           data_offset=0,
729                           full_knowledge=False,
730                           spherical=False):
731            """
732            :param datatype: data type indicator
733            :type datatype: ``DataSource.GRAVITY``, ``DataSource.MAGNETIC``
734            :param n_length: number of oscillation in the anomaly data within the observation region.
735            :type n_length: ``int``
736            :param n_depth: number of oscillation in the anomaly data below surface
737            :param shift: lateral phase shift in the  anomaly data
738            :param amplitude: data amplitude. Default value is 200 *U.kg/U.m**3 for gravity and 0.1 for magnetic data.
739            :param features: list of features. It is recommended that the features do entirely lay below surface.
740            :type features: ``list`` of ``SourceFeature``
741            :param DIM: spatial dimension
742            :type DIM: 2 or 3
743            :param number_of_elements: lateral number of elements in the region where data are collected
744            :type number_of_elements: ``int``
745            :param length: lateral extend of the region where data are collected
746            :type length: ``float``
747            :param B_b: background magnetic flux density [B_r, B_latiude, B_longitude]. Only used for magnetic data.
748            :type B_b: ``list`` of ``Scalar``
749            :param data_offset: offset of the data collection region from the surface
750            :type data_offset: ``float``
751            :param full_knowledge: if ``True`` data are collected from the entire subsurface region. This is mainly for testing.
752            :type full_knowledge: ``Bool``
753            :param spherical: if ``True`` sperical coordinates are used (ignored)
754            :type spherical: ``Bool``
755            """      
756            super(SyntheticData,self).__init__(
757                                     datatype=datatype, DIM=DIM, number_of_elements=number_of_elements,
758                                     length=length, B_b=B_b,
759                                     data_offset=data_offset,
760                                     full_knowledge=full_knowledge,
761                                     spherical=spherical)
762            self.__n_length = n_length
763            self.__n_depth = n_depth
764            self.__shift = shift
765            if amplitude == None:
766            if datatype == DataSource.GRAVITY:
767                amplitude = 200 *U.kg/U.m**3
768            else:
769                 amplitude =0.1
770            self.__amplitude = amplitude
771    
772    
773    
774        def getReferenceProperty(self, domain=None):
775            """
776            Returns the reference density Data object that was used to generate
777            the gravity anomaly data.
778            """
779            if self._reference_data == None:
780                DIM=domain.getDim()
781                x=domain.getX()
782                # set the reference data
783                z=x[DIM-1]
784                k=sin(self.__n_depth * np.pi/inf(z) * z) * whereNegative(z) * self.__amplitude
785                for i in xrange(DIM-1):
786               k*= sin(self.__n_length * np.pi /self.__length * x[i] - shift )
787                self._reference_data= k
788            return self._reference_data
789    
790    

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