/[escript]/trunk/downunder/py_src/datasources.py
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revision 4044 by caltinay, Tue Oct 30 03:35:17 2012 UTC revision 4108 by caltinay, Thu Dec 13 06:38:11 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__ = ['simpleBackgroundMagneticField', 'DataSource','ErMapperData','SyntheticFeatureData','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
# Line 34  from esys.ripley import Brick, Rectangle Line 34  from esys.ripley import Brick, Rectangle
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 78  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 simpleBackgroundMagneticField(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.      This class assumes 2D data which is mapped to a slice of a 3D domain.
94      For other setups override the `_createDomain` method.      For other setups override the methods as required.
95      """      """
96    
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):  
         """  
         Sets the density mask to use.  
         """  
         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):  
         """  
         Sets the susceptibility mask to use.  
         """  
         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 that supply  
         gravity data.  
         """  
         raise NotImplementedError  
   
     def getMagneticFieldAndStdDev(self):  
         """  
         Returns the magnetic field and standard deviation data objects as a  
         tuple. This method must be implemented in subclasses that supply  
         magnetic data.  
         """  
         raise NotImplementedError  
   
     def getBackgroundMagneticField(self):  
         """  
         Returns the background magnetic field. This method must be  
         implemented in subclasses that supply magnetic data.  
         """  
         return NotImplementedError  
106    
107      def getDataExtents(self):      def getDataExtents(self):
108          """          """
# Line 216  class DataSource(object): Line 116  class DataSource(object):
116          """          """
117          raise NotImplementedError          raise NotImplementedError
118    
119      def getVerticalExtents(self):      def getDataType(self):
120          """          """
121          returns a tuple ``(z0, nz, dz)``, where          Returns the type of survey data managed by this source.
122            Subclasses must return `GRAVITY` or `MAGNETIC` as appropriate.
         - ``z0`` = minimum z coordinate (origin)  
         - ``nz`` = number of elements in z direction  
         - ``dz`` = spacing of elements (= cell size in z)  
   
         This method must be implemented in subclasses.  
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 range(DIM)]  
         NEdiff=[NE_new[i]-NE[i] for i in range(DIM)]  
         spacing=[l[i]/NE[i] for i in range(DIM)]  
         l_new=[NE_new[i]*spacing[i] for i in range(DIM)]  
         origin_new=[origin[i]-NEdiff[i]/2.*spacing[i] for i in range(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 range(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 range(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 range(dim)]  
             index=int(idx_mult.dot(index))  
             for i in range(num_arrays):  
                 for p in range(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 range(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 range(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 range(3)]  
         dom=Brick(*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 range(3)]  
         self._dom_NE=[int(self._dom_len[i]/self._spacing[i]) for i in range(3)]  
         x=dom.getX()-[self._dom_origin[i]+NE_pad[i]*self._spacing[i] for i in range(3)]  
         mask=wherePositive(dom.getX()[2])  
   
         # prepare density mask (=1 at padding area, 0 else)  
         if self._constrainSides:  
             for i in range(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, meshfile, gravfile, topofile=None):  
         super(UBCDataSource,self).__init__()  
         self.__meshfile=meshfile  
         self.__gravfile=gravfile  
         self.__topofile=topofile  
         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 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 range(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 range(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):  
     """  
     Data Source for gridded netCDF data that use CF/COARDS conventions.  
     """  
     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  
142          """          """
143          super(NetCDFDataSource,self).__init__()          raise NotImplementedError
         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 range(3)]  
         self.__wkt_string=wkt_string  
         self.__lon=lon_name  
         self.__lat=lat_name  
         self.__grv=grav_name  
         self.__maskval=maskval  
144    
145      def getDataExtents(self):      def setSubsamplingFactor(self, f):
146          """          """
147          returns ( (x0, y0), (nx, ny), (dx, dy) )          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 getVerticalExtents(self):      def getSubsamplingFactor(self):
157          """          """
158          returns (z0, nz, dz)          Returns the subsampling factor that was set via `setSubsamplingFactor`
159            (see there).
160          """          """
161          return (self.__origin[2], self.__nPts[2], self.__delta[2])          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
# Line 696  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 getDataType(self):
296            return self.__datatype
297    
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      def getVerticalExtents(self):          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          returns (z0, nz, dz)          :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            # 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          return (self.__origin[2], self.__nPts[2], self.__delta[2])          returns ( (x0, y0), (nx, ny), (dx, dy) )
438            """
439            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 733  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 748  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
         self.k_outer=k_outer  
         self.rho=None  
         self.k=None  
492          self.mask=None          self.mask=None
493    
494      def getDensity(self,x):      def getValue(self,x):
495          if self.rho is None:          if self.value is None:
496              if self.rho_outer is None or self.rho_inner is None:              if self.v_outer is None or self.v_inner is None:
497                  self.rho=0                  self.value=0
498              else:              else:
499                  DIM=x.getDomain().getDim()                  DIM=x.getDomain().getDim()
500                  alpha=-log(abs(self.rho_outer/self.rho_inner))*4                  alpha=-log(abs(self.v_outer/self.v_inner))*4
501                  rho=exp(-alpha*((x[0]-self.x)/self.lx)**2)                  value=exp(-alpha*((x[0]-self.x)/self.lx)**2)
502                  rho=rho*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.rho=maximum(abs(self.rho_outer), abs(self.rho_inner*rho))                  self.value=maximum(abs(self.v_outer), abs(self.v_inner*value))
504                  if self.rho_inner<0: self.rho=-self.rho                  if self.v_inner<0: self.value=-self.value
   
         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  
             else:  
                 DIM=x.getDomain().getDim()  
                 alpha=-log(abs(self.k_outer/self.k_inner))*4  
                 k=exp(-alpha*((x[0]-self.x)/self.lx)**2)  
                 k=k*exp(-alpha*((x[DIM-1]-(sup(x[DIM-1])-self.depth))/self.lz)**2)  
                 self.k=maximum(abs(self.k_outer), abs(self.k_inner*k))  
                 if self.k_inner<0: self.k=-self.k  
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))
# Line 801  class SmoothAnomaly(SourceFeature): Line 515  class SmoothAnomaly(SourceFeature):
515          return m          return m
516    
517  ##############################################################################  ##############################################################################
518  class SyntheticDataSource(DataSource):  class SyntheticFeatureData(DataSource):
519      def __init__(self, DIM, NE, l, h, features, latitude=-32.):      def __init__(self, datatype, DIM, NE, l, features, B_b=None):
520          super(SyntheticDataSource,self).__init__()          super(SyntheticFeatureData,self).__init__()
521            if not datatype in [self.GRAVITY,self.MAGNETIC]:
522                raise ValueError("Invalid value for datatype parameter")
523            self.__datatype = datatype
524          self._features = features          self._features = features
525            self.__origin = [0]*(DIM-1)
526            self.__nPts = [NE]*(DIM-1)
527            self.__delta = [float(l)/NE]*(DIM-1)
528            self.__B_b =None
529          self.DIM=DIM          self.DIM=DIM
530          self.NE=NE          self.NE=NE
531          self.l=l          self.l=l
532          self.h=h          # this is for Cartesian (FIXME ?)
533          self.latitude=latitude          if datatype  ==  self.MAGNETIC:
534                if self.DIM<3:
535      def _createDomain(self, padding_x, padding_y):                 self.__B_b =  np.array([-B_b[2],  -B_b[0]])
536          NE_H=self.NE              else:
537          NE_L=int((self.l/self.h)*NE_H+0.5)                 self.__B_b = ([-B_b[1],  -B_b[2],  -B_b[0]])
         l=[self.l]*(self.DIM-1)+[self.h]  
         NE=[NE_L]*(self.DIM-1)+[NE_H]  
         origin=[0.]*self.DIM  
         NE_new, l_new, origin_new = self._addPadding(padding_x, padding_y, \  
                 NE, l, origin)  
   
         self.NE=NE_new  
         self.l=l_new[0]  
         self.h=l_new[self.DIM-1]  
   
         self.logger.debug("Data Source: synthetic with %d features"%len(self._features))  
         if self.DIM==2:  
             dom = Rectangle(n0=NE_new[0], n1=NE_new[1], l0=l_new[0], l1=l_new[1])  
             self._x = dom.getX() + origin_new  
             self.logger.debug("Domain size: %d x %d elements"%(NE_new[0], NE_new[1]))  
             self.logger.debug("     length: %g x %g"%(l_new[0],l_new[1]))  
             self.logger.debug("     origin: %g x %g"%(origin_new[0],origin_new[1]))  
         else:  
             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])  
             self._x = dom.getX() + origin_new  
             self.logger.debug("Domain size: %d x %d x %d elements"%(self.NE[0],self.NE[1],self.NE[2]))  
             self.logger.debug("     length: %g x %g x %g"%(l_new[0],l_new[1],l_new[2]))  
             self.logger.debug("     origin: %g x %g x %g"%(origin_new[0],origin_new[1],origin_new[2]))  
   
         dz=l_new[self.DIM-1]/NE_new[self.DIM-1]  
         self._g_mask=wherePositive(dom.getX()[0]-origin_new[0]) \  
                 * whereNegative(dom.getX()[0]-(l_new[0]-origin_new[0])) \  
                 * whereNonNegative(dom.getX()[self.DIM-1]-(l_new[self.DIM-1]+origin_new[self.DIM-1])) \  
                 * whereNonPositive(dom.getX()[self.DIM-1]-(l_new[self.DIM-1]+(origin_new[self.DIM-1]+dz)))  
   
         self._B_mask=self._g_mask  
   
         mask=whereNegative(self._x[self.DIM-1]) + \  
                 wherePositive(self._x[self.DIM-1]-l[self.DIM-1])  
         for i in range(self.DIM-1):  
             mask+= whereNegative(self._x[i]) +  wherePositive(self._x[i]-l[i])  
         self.setSetDensityMask(wherePositive(mask))  
         self.setSetSusceptibilityMask(wherePositive(mask))  
   
         rho_ref=0.  
         k_ref=0  
         for f in self._features:  
             m=f.getMask(self._x)  
             rho_ref = rho_ref * (1-m) + f.getDensity(self._x) * m  
             k_ref = k_ref * (1-m) + f.getSusceptibility(self._x) * m  
         self._rho=rho_ref  
         self._k=k_ref  
538    
539          return dom      def getDataExtents(self):
540            return (list(self.__origin), list(self.__nPts), list(self.__delta))
541    
542        def getDataType(self):
543            return self.__datatype
544    
545      def getReferenceDensity(self):      def getReferenceDensity(self):
546            """
547            Returns the reference density Data object that was used to generate
548            the gravity anomaly data.
549            """
550          return self._rho          return self._rho
551    
552      def getReferenceSusceptibility(self):      def getReferenceSusceptibility(self):
553            """
554            Returns the reference magnetic susceptibility Data objects that was
555            used to generate the magnetic field data.
556            """
557          return self._k          return self._k
558    
559      def getGravityAndStdDev(self):      def getSurveyData(self, domain, origin, NE, spacing):
560          pde=LinearSinglePDE(self.getDomain())          pde=LinearSinglePDE(domain)
561          G=U.Gravitational_Constant          G=U.Gravitational_Constant
562          m_psi_ref=0.          m_psi_ref=0.
563          for i in range(self.DIM):          x=domain.getX()
564              m_psi_ref=m_psi_ref + whereZero(self._x[i]-inf(self._x[i])) \          DIM=domain.getDim()
565                      + whereZero(self._x[i]-sup(self._x[i]))          m_psi_ref=whereZero(x[DIM-1]-sup(x[DIM-1])) # + whereZero(x[DIM-1]-inf(x[DIM-1]))
566            if self.getDataType()==DataSource.GRAVITY:
567          pde.setValue(A=kronecker(self.getDomain()), Y=-4*np.pi*G*self._rho, q=m_psi_ref)              rho_ref=0.
568          pde.setSymmetryOn()              for f in self._features:
569          psi_ref=pde.getSolution()                  m=f.getMask(x)
570          del pde                  rho_ref = rho_ref * (1-m) + f.getValue(x) * m
571          g=-grad(psi_ref)              self._rho=rho_ref
572          sigma=self._g_mask              pde.setValue(A=kronecker(domain), Y=-4*np.pi*G*rho_ref, q=m_psi_ref)
573          return g,sigma          else:
574                k_ref=0.
575      def getMagneticFieldAndStdDev(self):              for f in self._features:
576          pde=LinearSinglePDE(self.getDomain())                  m=f.getMask(x)
577          B_b=self.getBackgroundMagneticField()                  k_ref = k_ref * (1-m) + f.getValue(x) * m
578          DIM=self.getDomain().getDim()              self._k=k_ref
579          m_psi_ref=0.              pde.setValue(A=kronecker(domain), X=k_ref*self.__B_b, q=m_psi_ref)
         for i in range(self.DIM):  
             m_psi_ref=m_psi_ref + whereZero(self._x[i]-inf(self._x[i])) \  
                     + whereZero(self._x[i]-sup(self._x[i]))  
         pde.setValue(A=kronecker(self.getDomain()), X=(1+self._k)*B_b, q=m_psi_ref)  
580          pde.setSymmetryOn()          pde.setSymmetryOn()
581          psi_ref=pde.getSolution()          psi_ref=pde.getSolution()
582          del pde          del pde
583          B= (1+self._k) * B_b -grad(psi_ref)          if self.getDataType()==DataSource.GRAVITY:
584          sigma=self._B_mask              data = -grad(psi_ref, ReducedFunction(domain))
         return B,sigma  
   
     def getBackgroundMagneticField(self):  
         theta = (90-self.latitude)/180.*np.pi  
         B_0=U.Mu_0  * U.Magnetic_Dipole_Moment_Earth / (4 * np.pi *  U.R_Earth**3)  
         B_theta= B_0 * sin(theta)  
         B_r= 2 * B_0 * cos(theta)  
         DIM=self.getDomain().getDim()  
         if DIM<3:  
             return np.array([0.,  -B_r])  
585          else:          else:
586              return np.array([-B_theta, 0.,  -B_r])              data = self._k*self.__B_b-grad(psi_ref, ReducedFunction(domain))
587    
588            sigma=1.
589            # limit mask to non-padding in horizontal area
590            x=ReducedFunction(domain).getX()
591            for i in range(self.DIM-1):
592                sigma=sigma * wherePositive(x[i]) \
593                            * whereNegative(x[i]-(sup(x[i])+inf(x[i])))
594            # limit mask to one cell thickness at z=0
595            sigma = sigma * whereNonNegative(x[self.DIM-1]) \
596                    * whereNonPositive(x[self.DIM-1]-spacing[self.DIM-1])
597            return data,sigma
598    
599    
600    

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