idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
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44,000 | def _get_SRF_sigma ( self , imt_per ) : if imt_per < 0.6 : srf = 0.8 elif 0.6 <= imt_per < 1 : srf = self . _interp_function ( 0.7 , 0.8 , 1 , 0.6 , imt_per ) elif 1 <= imt_per <= 10 : srf = self . _interp_function ( 0.6 , 0.7 , 10 , 1 , imt_per ) else : srf = 1 return srf | Table 8 and equation 19 of 2013 report . NB change in notation 2013 report calls this term sigma_t but it is referred to here as sigma . Note that Table 8 is identical to Table 7 in the 2013 report . |
44,001 | def _get_dL2L ( self , imt_per ) : if imt_per < 0.18 : dL2L = - 0.06 elif 0.18 <= imt_per < 0.35 : dL2L = self . _interp_function ( 0.12 , - 0.06 , 0.35 , 0.18 , imt_per ) elif 0.35 <= imt_per <= 10 : dL2L = self . _interp_function ( 0.65 , 0.12 , 10 , 0.35 , imt_per ) else : dL2L = 0 return dL2L | Table 3 and equation 19 of 2013 report . |
44,002 | def _get_dS2S ( self , imt_per ) : if imt_per == 0 : dS2S = 0.05 elif 0 < imt_per < 0.15 : dS2S = self . _interp_function ( - 0.15 , 0.05 , 0.15 , 0 , imt_per ) elif 0.15 <= imt_per < 0.45 : dS2S = self . _interp_function ( 0.4 , - 0.15 , 0.45 , 0.15 , imt_per ) elif 0.45 <= imt_per < 3.2 : dS2S = 0.4 elif 3.2 <= imt_per < 5 : dS2S = self . _interp_function ( 0.08 , 0.4 , 5 , 3.2 , imt_per ) elif 5 <= imt_per <= 10 : dS2S = 0.08 else : dS2S = 0 return dS2S | Table 4 of 2013 report |
44,003 | def context ( src ) : try : yield except Exception : etype , err , tb = sys . exc_info ( ) msg = 'An error occurred with source id=%s. Error: %s' msg %= ( src . source_id , err ) raise_ ( etype , msg , tb ) | Used to add the source_id to the error message . To be used as |
44,004 | def get_bounding_box ( self , lon , lat , trt = None , mag = None ) : if trt is None : maxdist = max ( self ( trt , mag ) for trt in self . dic ) else : maxdist = self ( trt , mag ) a1 = min ( maxdist * KM_TO_DEGREES , 90 ) a2 = min ( angular_distance ( maxdist , lat ) , 180 ) return lon - a2 , lat - a1 , lon + a2 , lat + a1 | Build a bounding box around the given lon lat by computing the maximum_distance at the given tectonic region type and magnitude . |
44,005 | def get_affected_box ( self , src ) : mag = src . get_min_max_mag ( ) [ 1 ] maxdist = self ( src . tectonic_region_type , mag ) bbox = get_bounding_box ( src , maxdist ) return ( fix_lon ( bbox [ 0 ] ) , bbox [ 1 ] , fix_lon ( bbox [ 2 ] ) , bbox [ 3 ] ) | Get the enlarged bounding box of a source . |
44,006 | def sitecol ( self ) : if 'sitecol' in vars ( self ) : return self . __dict__ [ 'sitecol' ] if self . filename is None or not os . path . exists ( self . filename ) : return with hdf5 . File ( self . filename , 'r' ) as h5 : self . __dict__ [ 'sitecol' ] = sc = h5 . get ( 'sitecol' ) return sc | Read the site collection from . filename and cache it |
44,007 | def hypocentre_patch_index ( cls , hypocentre , rupture_top_edge , upper_seismogenic_depth , lower_seismogenic_depth , dip ) : totaln_patch = len ( rupture_top_edge ) indexlist = [ ] dist_list = [ ] for i , index in enumerate ( range ( 1 , totaln_patch ) ) : p0 , p1 , p2 , p3 = cls . get_fault_patch_vertices ( rupture_top_edge , upper_seismogenic_depth , lower_seismogenic_depth , dip , index_patch = index ) [ normal , dist_to_plane ] = get_plane_equation ( p0 , p1 , p2 , hypocentre ) indexlist . append ( index ) dist_list . append ( dist_to_plane ) if numpy . allclose ( dist_to_plane , 0. , atol = 25. , rtol = 0. ) : return index break index = indexlist [ numpy . argmin ( dist_list ) ] return index | This methods finds the index of the fault patch including the hypocentre . |
44,008 | def get_surface_vertexes ( cls , fault_trace , upper_seismogenic_depth , lower_seismogenic_depth , dip ) : dip_tan = math . tan ( math . radians ( dip ) ) hdist_top = upper_seismogenic_depth / dip_tan hdist_bottom = lower_seismogenic_depth / dip_tan strike = fault_trace [ 0 ] . azimuth ( fault_trace [ - 1 ] ) azimuth = ( strike + 90.0 ) % 360 lons = [ ] lats = [ ] for point in fault_trace . points : top_edge_point = point . point_at ( hdist_top , 0 , azimuth ) bottom_edge_point = point . point_at ( hdist_bottom , 0 , azimuth ) lons . append ( top_edge_point . longitude ) lats . append ( top_edge_point . latitude ) lons . append ( bottom_edge_point . longitude ) lats . append ( bottom_edge_point . latitude ) lons = numpy . array ( lons , float ) lats = numpy . array ( lats , float ) return lons , lats | Get surface main vertexes . |
44,009 | def surface_projection_from_fault_data ( cls , fault_trace , upper_seismogenic_depth , lower_seismogenic_depth , dip ) : lons , lats = cls . get_surface_vertexes ( fault_trace , upper_seismogenic_depth , lower_seismogenic_depth , dip ) return Mesh ( lons , lats , depths = None ) . get_convex_hull ( ) | Get a surface projection of the simple fault surface . |
44,010 | def _compute_distance_term ( self , C , mag , rrup ) : term1 = C [ 'b' ] * rrup term2 = - np . log ( rrup + C [ 'c' ] * np . exp ( C [ 'd' ] * mag ) ) return term1 + term2 | Compute second and third terms in equation 1 p . 901 . |
44,011 | def _compute_focal_depth_term ( self , C , hypo_depth ) : focal_depth = hypo_depth if focal_depth > 125.0 : focal_depth = 125.0 hc = 15.0 return float ( focal_depth >= hc ) * C [ 'e' ] * ( focal_depth - hc ) | Compute fourth term in equation 1 p . 901 . |
44,012 | def _compute_site_class_term ( self , C , vs30 ) : site_term = np . zeros ( len ( vs30 ) ) site_term [ vs30 > 1100.0 ] = C [ 'CH' ] site_term [ ( vs30 > 600 ) & ( vs30 <= 1100 ) ] = C [ 'C1' ] site_term [ ( vs30 > 300 ) & ( vs30 <= 600 ) ] = C [ 'C2' ] site_term [ ( vs30 > 200 ) & ( vs30 <= 300 ) ] = C [ 'C3' ] site_term [ vs30 <= 200 ] = C [ 'C4' ] return site_term | Compute nine - th term in equation 1 p . 901 . |
44,013 | def _compute_magnitude_squared_term ( self , P , M , Q , W , mag ) : return P * ( mag - M ) + Q * ( mag - M ) ** 2 + W | Compute magnitude squared term equation 5 p . 909 . |
44,014 | def _compute_slab_correction_term ( self , C , rrup ) : slab_term = C [ 'SSL' ] * np . log ( rrup ) return slab_term | Compute path modification term for slab events that is the 8 - th term in equation 1 p . 901 . |
44,015 | def get_mean_and_stddevs ( self , sites , rup , dists , imt , stddev_types ) : dists_mod = copy . deepcopy ( dists ) dists_mod . rrup [ dists . rrup <= 5. ] = 5. return super ( ) . get_mean_and_stddevs ( sites , rup , dists_mod , imt , stddev_types ) | Using a minimum distance of 5km for the calculation . |
44,016 | def confirm ( prompt ) : while True : try : answer = input ( prompt ) except KeyboardInterrupt : return False answer = answer . strip ( ) . lower ( ) if answer not in ( 'y' , 'n' ) : print ( 'Please enter y or n' ) continue return answer == 'y' | Ask for confirmation given a prompt and return a boolean value . |
44,017 | def _csv_header ( self ) : fields = [ 'id' , 'number' , 'taxonomy' , 'lon' , 'lat' ] for name in self . cost_types [ 'name' ] : fields . append ( name ) if 'per_area' in self . cost_types [ 'type' ] : fields . append ( 'area' ) if self . occupancy_periods : fields . extend ( self . occupancy_periods . split ( ) ) fields . extend ( self . tagcol . tagnames ) return set ( fields ) | Extract the expected CSV header from the exposure metadata |
44,018 | def build_vf_node ( vf ) : nodes = [ Node ( 'imls' , { 'imt' : vf . imt } , vf . imls ) , Node ( 'meanLRs' , { } , vf . mean_loss_ratios ) , Node ( 'covLRs' , { } , vf . covs ) ] return Node ( 'vulnerabilityFunction' , { 'id' : vf . id , 'dist' : vf . distribution_name } , nodes = nodes ) | Convert a VulnerabilityFunction object into a Node suitable for XML conversion . |
44,019 | def get_riskmodel ( taxonomy , oqparam , ** extra ) : riskmodel_class = registry [ oqparam . calculation_mode ] argnames = inspect . getfullargspec ( riskmodel_class . __init__ ) . args [ 3 : ] known_args = set ( name for name , value in inspect . getmembers ( oqparam . __class__ ) if isinstance ( value , valid . Param ) ) all_args = { } for argname in argnames : if argname in known_args : all_args [ argname ] = getattr ( oqparam , argname ) if 'hazard_imtls' in argnames : all_args [ 'hazard_imtls' ] = oqparam . imtls all_args . update ( extra ) missing = set ( argnames ) - set ( all_args ) if missing : raise TypeError ( 'Missing parameter: %s' % ', ' . join ( missing ) ) return riskmodel_class ( taxonomy , ** all_args ) | Return an instance of the correct riskmodel class depending on the attribute calculation_mode of the object oqparam . |
44,020 | def Beachball ( fm , linewidth = 2 , facecolor = 'b' , bgcolor = 'w' , edgecolor = 'k' , alpha = 1.0 , xy = ( 0 , 0 ) , width = 200 , size = 100 , nofill = False , zorder = 100 , outfile = None , format = None , fig = None ) : plot_width = width * 0.95 if not fig : fig = plt . figure ( figsize = ( 3 , 3 ) , dpi = 100 ) fig . subplots_adjust ( left = 0 , bottom = 0 , right = 1 , top = 1 ) fig . set_figheight ( width // 100 ) fig . set_figwidth ( width // 100 ) ax = fig . add_subplot ( 111 , aspect = 'equal' ) ax . axison = False collection = Beach ( fm , linewidth = linewidth , facecolor = facecolor , edgecolor = edgecolor , bgcolor = bgcolor , alpha = alpha , nofill = nofill , xy = xy , width = plot_width , size = size , zorder = zorder ) ax . add_collection ( collection ) ax . autoscale_view ( tight = False , scalex = True , scaley = True ) if outfile : if format : fig . savefig ( outfile , dpi = 100 , transparent = True , format = format ) else : fig . savefig ( outfile , dpi = 100 , transparent = True ) elif format and not outfile : imgdata = compatibility . BytesIO ( ) fig . savefig ( imgdata , format = format , dpi = 100 , transparent = True ) imgdata . seek ( 0 ) return imgdata . read ( ) else : plt . show ( ) return fig | Draws a beach ball diagram of an earthquake focal mechanism . |
44,021 | def StrikeDip ( n , e , u ) : r2d = 180 / np . pi if u < 0 : n = - n e = - e u = - u strike = np . arctan2 ( e , n ) * r2d strike = strike - 90 while strike >= 360 : strike = strike - 360 while strike < 0 : strike = strike + 360 x = np . sqrt ( np . power ( n , 2 ) + np . power ( e , 2 ) ) dip = np . arctan2 ( x , u ) * r2d return ( strike , dip ) | Finds strike and dip of plane given normal vector having components n e and u . |
44,022 | def AuxPlane ( s1 , d1 , r1 ) : r2d = 180 / np . pi z = ( s1 + 90 ) / r2d z2 = d1 / r2d z3 = r1 / r2d sl1 = - np . cos ( z3 ) * np . cos ( z ) - np . sin ( z3 ) * np . sin ( z ) * np . cos ( z2 ) sl2 = np . cos ( z3 ) * np . sin ( z ) - np . sin ( z3 ) * np . cos ( z ) * np . cos ( z2 ) sl3 = np . sin ( z3 ) * np . sin ( z2 ) ( strike , dip ) = StrikeDip ( sl2 , sl1 , sl3 ) n1 = np . sin ( z ) * np . sin ( z2 ) n2 = np . cos ( z ) * np . sin ( z2 ) h1 = - sl2 h2 = sl1 z = h1 * n1 + h2 * n2 z = z / np . sqrt ( h1 * h1 + h2 * h2 ) z = np . arccos ( z ) rake = 0 if sl3 > 0 : rake = z * r2d if sl3 <= 0 : rake = - z * r2d return ( strike , dip , rake ) | Get Strike and dip of second plane . |
44,023 | def MT2Plane ( mt ) : ( d , v ) = np . linalg . eig ( mt . mt ) D = np . array ( [ d [ 1 ] , d [ 0 ] , d [ 2 ] ] ) V = np . array ( [ [ v [ 1 , 1 ] , - v [ 1 , 0 ] , - v [ 1 , 2 ] ] , [ v [ 2 , 1 ] , - v [ 2 , 0 ] , - v [ 2 , 2 ] ] , [ - v [ 0 , 1 ] , v [ 0 , 0 ] , v [ 0 , 2 ] ] ] ) IMAX = D . argmax ( ) IMIN = D . argmin ( ) AE = ( V [ : , IMAX ] + V [ : , IMIN ] ) / np . sqrt ( 2.0 ) AN = ( V [ : , IMAX ] - V [ : , IMIN ] ) / np . sqrt ( 2.0 ) AER = np . sqrt ( np . power ( AE [ 0 ] , 2 ) + np . power ( AE [ 1 ] , 2 ) + np . power ( AE [ 2 ] , 2 ) ) ANR = np . sqrt ( np . power ( AN [ 0 ] , 2 ) + np . power ( AN [ 1 ] , 2 ) + np . power ( AN [ 2 ] , 2 ) ) AE = AE / AER if not ANR : AN = np . array ( [ np . nan , np . nan , np . nan ] ) else : AN = AN / ANR if AN [ 2 ] <= 0. : AN1 = AN AE1 = AE else : AN1 = - AN AE1 = - AE ( ft , fd , fl ) = TDL ( AN1 , AE1 ) return NodalPlane ( 360 - ft , fd , 180 - fl ) | Calculates a nodal plane of a given moment tensor . |
44,024 | def TDL ( AN , BN ) : XN = AN [ 0 ] YN = AN [ 1 ] ZN = AN [ 2 ] XE = BN [ 0 ] YE = BN [ 1 ] ZE = BN [ 2 ] AAA = 1.0 / ( 1000000 ) CON = 57.2957795 if np . fabs ( ZN ) < AAA : FD = 90. AXN = np . fabs ( XN ) if AXN > 1.0 : AXN = 1.0 FT = np . arcsin ( AXN ) * CON ST = - XN CT = YN if ST >= 0. and CT < 0 : FT = 180. - FT if ST < 0. and CT <= 0 : FT = 180. + FT if ST < 0. and CT > 0 : FT = 360. - FT FL = np . arcsin ( abs ( ZE ) ) * CON SL = - ZE if np . fabs ( XN ) < AAA : CL = XE / YN else : CL = - YE / XN if SL >= 0. and CL < 0 : FL = 180. - FL if SL < 0. and CL <= 0 : FL = FL - 180. if SL < 0. and CL > 0 : FL = - FL else : if - ZN > 1.0 : ZN = - 1.0 FDH = np . arccos ( - ZN ) FD = FDH * CON SD = np . sin ( FDH ) if SD == 0 : return ST = - XN / SD CT = YN / SD SX = np . fabs ( ST ) if SX > 1.0 : SX = 1.0 FT = np . arcsin ( SX ) * CON if ST >= 0. and CT < 0 : FT = 180. - FT if ST < 0. and CT <= 0 : FT = 180. + FT if ST < 0. and CT > 0 : FT = 360. - FT SL = - ZE / SD SX = np . fabs ( SL ) if SX > 1.0 : SX = 1.0 FL = np . arcsin ( SX ) * CON if ST == 0 : CL = XE / CT else : XXX = YN * ZN * ZE / SD / SD + YE CL = - SD * XXX / XN if CT == 0 : CL = YE / ST if SL >= 0. and CL < 0 : FL = 180. - FL if SL < 0. and CL <= 0 : FL = FL - 180. if SL < 0. and CL > 0 : FL = - FL return ( FT , FD , FL ) | Helper function for MT2Plane . |
44,025 | def MT2Axes ( mt ) : ( D , V ) = np . linalg . eigh ( mt . mt ) pl = np . arcsin ( - V [ 0 ] ) az = np . arctan2 ( V [ 2 ] , - V [ 1 ] ) for i in range ( 0 , 3 ) : if pl [ i ] <= 0 : pl [ i ] = - pl [ i ] az [ i ] += np . pi if az [ i ] < 0 : az [ i ] += 2 * np . pi if az [ i ] > 2 * np . pi : az [ i ] -= 2 * np . pi pl *= R2D az *= R2D T = PrincipalAxis ( D [ 2 ] , az [ 2 ] , pl [ 2 ] ) N = PrincipalAxis ( D [ 1 ] , az [ 1 ] , pl [ 1 ] ) P = PrincipalAxis ( D [ 0 ] , az [ 0 ] , pl [ 0 ] ) return ( T , N , P ) | Calculates the principal axes of a given moment tensor . |
44,026 | def tapered_gutenberg_richter_cdf ( moment , moment_threshold , beta , corner_moment ) : cdf = np . exp ( ( moment_threshold - moment ) / corner_moment ) return ( ( moment / moment_threshold ) ** ( - beta ) ) * cdf | Tapered Gutenberg Richter Cumulative Density Function |
44,027 | def tapered_gutenberg_richter_pdf ( moment , moment_threshold , beta , corner_moment ) : return ( ( beta / moment + 1. / corner_moment ) * tapered_gutenberg_richter_cdf ( moment , moment_threshold , beta , corner_moment ) ) | Tapered Gutenberg - Richter Probability Density Function |
44,028 | def makedirs ( path ) : if os . path . exists ( path ) : if not os . path . isdir ( path ) : raise RuntimeError ( '%s already exists and is not a directory.' % path ) else : os . makedirs ( path ) | Make all of the directories in the path using os . makedirs . |
44,029 | def _get_observed_mmax ( catalogue , config ) : if config [ 'input_mmax' ] : obsmax = config [ 'input_mmax' ] if config [ 'input_mmax_uncertainty' ] : return config [ 'input_mmax' ] , config [ 'input_mmax_uncertainty' ] else : raise ValueError ( 'Input mmax uncertainty must be specified!' ) max_location = np . argmax ( catalogue [ 'magnitude' ] ) obsmax = catalogue [ 'magnitude' ] [ max_location ] cond = isinstance ( catalogue [ 'sigmaMagnitude' ] , np . ndarray ) and len ( catalogue [ 'sigmaMagnitude' ] ) > 0 and not np . all ( np . isnan ( catalogue [ 'sigmaMagnitude' ] ) ) if cond : if not np . isnan ( catalogue [ 'sigmaMagnitude' ] [ max_location ] ) : return obsmax , catalogue [ 'sigmaMagnitude' ] [ max_location ] else : print ( 'Uncertainty not given on observed Mmax\n' 'Taking largest magnitude uncertainty found in catalogue' ) return obsmax , np . nanmax ( catalogue [ 'sigmaMagnitude' ] ) elif config [ 'input_mmax_uncertainty' ] : return obsmax , config [ 'input_mmax_uncertainty' ] else : raise ValueError ( 'Input mmax uncertainty must be specified!' ) | Check see if observed mmax values are input if not then take from the catalogue |
44,030 | def _get_magnitude_vector_properties ( catalogue , config ) : mmin = config . get ( 'input_mmin' , np . min ( catalogue [ 'magnitude' ] ) ) neq = np . float ( np . sum ( catalogue [ 'magnitude' ] >= mmin - 1.E-7 ) ) return neq , mmin | If an input minimum magnitude is given then consider catalogue only above the minimum magnitude - returns corresponding properties |
44,031 | def get_dip ( self ) : if self . dip is None : mesh = self . mesh self . dip , self . strike = mesh . get_mean_inclination_and_azimuth ( ) return self . dip | Return the fault dip as the average dip over the mesh . |
44,032 | def check_surface_validity ( cls , edges ) : full_boundary = [ ] left_boundary = [ ] right_boundary = [ ] for i in range ( 1 , len ( edges ) - 1 ) : left_boundary . append ( edges [ i ] . points [ 0 ] ) right_boundary . append ( edges [ i ] . points [ - 1 ] ) full_boundary . extend ( edges [ 0 ] . points ) full_boundary . extend ( right_boundary ) full_boundary . extend ( edges [ - 1 ] . points [ : : - 1 ] ) full_boundary . extend ( left_boundary [ : : - 1 ] ) lons = [ p . longitude for p in full_boundary ] lats = [ p . latitude for p in full_boundary ] depths = [ p . depth for p in full_boundary ] ul = edges [ 0 ] . points [ 0 ] strike = ul . azimuth ( edges [ 0 ] . points [ - 1 ] ) dist = 10. ur = ul . point_at ( dist , 0 , strike ) bl = Point ( ul . longitude , ul . latitude , ul . depth + dist ) br = bl . point_at ( dist , 0 , strike ) ref_plane = PlanarSurface . from_corner_points ( ul , ur , br , bl ) _ , xx , yy = ref_plane . _project ( spherical_to_cartesian ( lons , lats , depths ) ) coords = [ ( x , y ) for x , y in zip ( xx , yy ) ] p = shapely . geometry . Polygon ( coords ) if not p . is_valid : raise ValueError ( 'Edges points are not in the right order' ) | Check validity of the surface . |
44,033 | def surface_projection_from_fault_data ( cls , edges ) : lons = [ ] lats = [ ] for edge in edges : for point in edge : lons . append ( point . longitude ) lats . append ( point . latitude ) lons = numpy . array ( lons , dtype = float ) lats = numpy . array ( lats , dtype = float ) return Mesh ( lons , lats , depths = None ) . get_convex_hull ( ) | Get a surface projection of the complex fault surface . |
44,034 | def check_time_event ( oqparam , occupancy_periods ) : time_event = oqparam . time_event if time_event and time_event not in occupancy_periods : raise ValueError ( 'time_event is %s in %s, but the exposure contains %s' % ( time_event , oqparam . inputs [ 'job_ini' ] , ', ' . join ( occupancy_periods ) ) ) | Check the time_event parameter in the datastore by comparing with the periods found in the exposure . |
44,035 | def get_idxs ( data , eid2idx ) : uniq , inv = numpy . unique ( data [ 'eid' ] , return_inverse = True ) idxs = numpy . array ( [ eid2idx [ eid ] for eid in uniq ] ) [ inv ] return idxs | Convert from event IDs to event indices . |
44,036 | def import_gmfs ( dstore , fname , sids ) : array = writers . read_composite_array ( fname ) . array imts = [ name [ 4 : ] for name in array . dtype . names [ 3 : ] ] n_imts = len ( imts ) gmf_data_dt = numpy . dtype ( [ ( 'rlzi' , U16 ) , ( 'sid' , U32 ) , ( 'eid' , U64 ) , ( 'gmv' , ( F32 , ( n_imts , ) ) ) ] ) eids = numpy . unique ( array [ 'eid' ] ) eids . sort ( ) E = len ( eids ) eid2idx = dict ( zip ( eids , range ( E ) ) ) events = numpy . zeros ( E , rupture . events_dt ) events [ 'eid' ] = eids dstore [ 'events' ] = events dic = general . group_array ( array . view ( gmf_data_dt ) , 'sid' ) lst = [ ] offset = 0 for sid in sids : n = len ( dic . get ( sid , [ ] ) ) lst . append ( ( offset , offset + n ) ) if n : offset += n gmvs = dic [ sid ] gmvs [ 'eid' ] = get_idxs ( gmvs , eid2idx ) gmvs [ 'rlzi' ] = 0 dstore . extend ( 'gmf_data/data' , gmvs ) dstore [ 'gmf_data/indices' ] = numpy . array ( lst , U32 ) dstore [ 'gmf_data/imts' ] = ' ' . join ( imts ) sig_eps_dt = [ ( 'eid' , U64 ) , ( 'sig' , ( F32 , n_imts ) ) , ( 'eps' , ( F32 , n_imts ) ) ] dstore [ 'gmf_data/sigma_epsilon' ] = numpy . zeros ( 0 , sig_eps_dt ) dstore [ 'weights' ] = numpy . ones ( ( 1 , n_imts ) ) return eids | Import in the datastore a ground motion field CSV file . |
44,037 | def save_params ( self , ** kw ) : if ( 'hazard_calculation_id' in kw and kw [ 'hazard_calculation_id' ] is None ) : del kw [ 'hazard_calculation_id' ] vars ( self . oqparam ) . update ( ** kw ) self . datastore [ 'oqparam' ] = self . oqparam attrs = self . datastore [ '/' ] . attrs attrs [ 'engine_version' ] = engine_version attrs [ 'date' ] = datetime . now ( ) . isoformat ( ) [ : 19 ] if 'checksum32' not in attrs : attrs [ 'checksum32' ] = readinput . get_checksum32 ( self . oqparam ) self . datastore . flush ( ) | Update the current calculation parameters and save engine_version |
44,038 | def run ( self , pre_execute = True , concurrent_tasks = None , close = True , ** kw ) : with self . _monitor : self . _monitor . username = kw . get ( 'username' , '' ) self . _monitor . hdf5 = self . datastore . hdf5 if concurrent_tasks is None : ct = self . oqparam . concurrent_tasks else : ct = concurrent_tasks if ct == 0 : oq_distribute = os . environ . get ( 'OQ_DISTRIBUTE' ) os . environ [ 'OQ_DISTRIBUTE' ] = 'no' if ct != self . oqparam . concurrent_tasks : self . oqparam . concurrent_tasks = ct self . save_params ( ** kw ) try : if pre_execute : self . pre_execute ( ) self . result = self . execute ( ) if self . result is not None : self . post_execute ( self . result ) self . before_export ( ) self . export ( kw . get ( 'exports' , '' ) ) except Exception : if kw . get ( 'pdb' ) : tb = sys . exc_info ( ) [ 2 ] traceback . print_tb ( tb ) pdb . post_mortem ( tb ) else : logging . critical ( '' , exc_info = True ) raise finally : if ct == 0 : if oq_distribute is None : del os . environ [ 'OQ_DISTRIBUTE' ] else : os . environ [ 'OQ_DISTRIBUTE' ] = oq_distribute readinput . pmap = None readinput . exposure = None readinput . gmfs = None readinput . eids = None self . _monitor . flush ( ) if close : self . result = None try : self . datastore . close ( ) except ( RuntimeError , ValueError ) : logging . warning ( '' , exc_info = True ) return getattr ( self , 'exported' , { } ) | Run the calculation and return the exported outputs . |
44,039 | def export ( self , exports = None ) : self . exported = getattr ( self . precalc , 'exported' , { } ) if isinstance ( exports , tuple ) : fmts = exports elif exports : fmts = exports . split ( ',' ) elif isinstance ( self . oqparam . exports , tuple ) : fmts = self . oqparam . exports else : fmts = self . oqparam . exports . split ( ',' ) keys = set ( self . datastore ) has_hcurves = ( 'hcurves-stats' in self . datastore or 'hcurves-rlzs' in self . datastore ) if has_hcurves : keys . add ( 'hcurves' ) for fmt in fmts : if not fmt : continue for key in sorted ( keys ) : if 'rlzs' in key and self . R > 1 : continue self . _export ( ( key , fmt ) ) if has_hcurves and self . oqparam . hazard_maps : self . _export ( ( 'hmaps' , fmt ) ) if has_hcurves and self . oqparam . uniform_hazard_spectra : self . _export ( ( 'uhs' , fmt ) ) | Export all the outputs in the datastore in the given export formats . Individual outputs are not exported if there are multiple realizations . |
44,040 | def before_export ( self ) : try : csm_info = self . datastore [ 'csm_info' ] except KeyError : csm_info = self . datastore [ 'csm_info' ] = self . csm . info for sm in csm_info . source_models : for sg in sm . src_groups : assert sg . eff_ruptures != - 1 , sg for key in self . datastore : self . datastore . set_nbytes ( key ) self . datastore . flush ( ) | Set the attributes nbytes |
44,041 | def read_inputs ( self ) : oq = self . oqparam self . _read_risk_data ( ) self . check_overflow ( ) if ( 'source_model_logic_tree' in oq . inputs and oq . hazard_calculation_id is None ) : self . csm = readinput . get_composite_source_model ( oq , self . monitor ( ) , srcfilter = self . src_filter ) self . init ( ) | Read risk data and sources if any |
44,042 | def pre_execute ( self ) : oq = self . oqparam if 'gmfs' in oq . inputs or 'multi_peril' in oq . inputs : assert not oq . hazard_calculation_id , ( 'You cannot use --hc together with gmfs_file' ) self . read_inputs ( ) if 'gmfs' in oq . inputs : save_gmfs ( self ) else : self . save_multi_peril ( ) elif 'hazard_curves' in oq . inputs : assert not oq . hazard_calculation_id , ( 'You cannot use --hc together with hazard_curves' ) haz_sitecol = readinput . get_site_collection ( oq ) self . load_riskmodel ( ) self . read_exposure ( haz_sitecol ) self . datastore [ 'poes/grp-00' ] = fix_ones ( readinput . pmap ) self . datastore [ 'sitecol' ] = self . sitecol self . datastore [ 'assetcol' ] = self . assetcol self . datastore [ 'csm_info' ] = fake = source . CompositionInfo . fake ( ) self . rlzs_assoc = fake . get_rlzs_assoc ( ) elif oq . hazard_calculation_id : parent = util . read ( oq . hazard_calculation_id ) self . check_precalc ( parent [ 'oqparam' ] . calculation_mode ) self . datastore . parent = parent params = { name : value for name , value in vars ( parent [ 'oqparam' ] ) . items ( ) if name not in vars ( self . oqparam ) } self . save_params ( ** params ) self . read_inputs ( ) oqp = parent [ 'oqparam' ] if oqp . investigation_time != oq . investigation_time : raise ValueError ( 'The parent calculation was using investigation_time=%s' ' != %s' % ( oqp . investigation_time , oq . investigation_time ) ) if oqp . minimum_intensity != oq . minimum_intensity : raise ValueError ( 'The parent calculation was using minimum_intensity=%s' ' != %s' % ( oqp . minimum_intensity , oq . minimum_intensity ) ) missing_imts = set ( oq . risk_imtls ) - set ( oqp . imtls ) if missing_imts : raise ValueError ( 'The parent calculation is missing the IMT(s) %s' % ', ' . join ( missing_imts ) ) elif self . __class__ . precalc : calc = calculators [ self . __class__ . precalc ] ( self . oqparam , self . datastore . calc_id ) calc . run ( ) self . param = calc . param self . sitecol = calc . sitecol self . assetcol = calc . assetcol self . riskmodel = calc . riskmodel if hasattr ( calc , 'rlzs_assoc' ) : self . rlzs_assoc = calc . rlzs_assoc else : raise InvalidFile ( '%(job_ini)s: missing gmfs_csv, multi_peril_csv' % oq . inputs ) if hasattr ( calc , 'csm' ) : self . csm = calc . csm else : self . read_inputs ( ) if self . riskmodel : self . save_riskmodel ( ) | Check if there is a previous calculation ID . If yes read the inputs by retrieving the previous calculation ; if not read the inputs directly . |
44,043 | def init ( self ) : oq = self . oqparam if not oq . risk_imtls : if self . datastore . parent : oq . risk_imtls = ( self . datastore . parent [ 'oqparam' ] . risk_imtls ) if 'precalc' in vars ( self ) : self . rlzs_assoc = self . precalc . rlzs_assoc elif 'csm_info' in self . datastore : csm_info = self . datastore [ 'csm_info' ] if oq . hazard_calculation_id and 'gsim_logic_tree' in oq . inputs : csm_info . gsim_lt = logictree . GsimLogicTree ( oq . inputs [ 'gsim_logic_tree' ] , set ( csm_info . trts ) ) self . rlzs_assoc = csm_info . get_rlzs_assoc ( ) elif hasattr ( self , 'csm' ) : self . check_floating_spinning ( ) self . rlzs_assoc = self . csm . info . get_rlzs_assoc ( ) else : self . datastore [ 'csm_info' ] = fake = source . CompositionInfo . fake ( ) self . rlzs_assoc = fake . get_rlzs_assoc ( ) | To be overridden to initialize the datasets needed by the calculation |
44,044 | def read_exposure ( self , haz_sitecol = None ) : with self . monitor ( 'reading exposure' , autoflush = True ) : self . sitecol , self . assetcol , discarded = ( readinput . get_sitecol_assetcol ( self . oqparam , haz_sitecol , self . riskmodel . loss_types ) ) if len ( discarded ) : self . datastore [ 'discarded' ] = discarded if hasattr ( self , 'rup' ) : logging . info ( '%d assets were discarded because too far ' 'from the rupture; use `oq show discarded` ' 'to show them and `oq plot_assets` to plot ' 'them' % len ( discarded ) ) elif not self . oqparam . discard_assets : self . datastore [ 'sitecol' ] = self . sitecol self . datastore [ 'assetcol' ] = self . assetcol raise RuntimeError ( '%d assets were discarded; use `oq show discarded` to' ' show them and `oq plot_assets` to plot them' % len ( discarded ) ) taxonomies = set ( taxo for taxo in self . assetcol . tagcol . taxonomy if taxo != '?' ) if len ( self . riskmodel . taxonomies ) > len ( taxonomies ) : logging . info ( 'Reducing risk model from %d to %d taxonomies' , len ( self . riskmodel . taxonomies ) , len ( taxonomies ) ) self . riskmodel = self . riskmodel . reduce ( taxonomies ) return readinput . exposure | Read the exposure the riskmodel and update the attributes . sitecol . assetcol |
44,045 | def save_riskmodel ( self ) : self . datastore [ 'risk_model' ] = rm = self . riskmodel self . datastore [ 'taxonomy_mapping' ] = self . riskmodel . tmap attrs = self . datastore . getitem ( 'risk_model' ) . attrs attrs [ 'min_iml' ] = hdf5 . array_of_vstr ( sorted ( rm . min_iml . items ( ) ) ) self . datastore . set_nbytes ( 'risk_model' ) | Save the risk models in the datastore |
44,046 | def store_rlz_info ( self , eff_ruptures = None ) : if hasattr ( self , 'csm' ) : self . csm . info . update_eff_ruptures ( eff_ruptures ) self . rlzs_assoc = self . csm . info . get_rlzs_assoc ( self . oqparam . sm_lt_path ) if not self . rlzs_assoc : raise RuntimeError ( 'Empty logic tree: too much filtering?' ) self . datastore [ 'csm_info' ] = self . csm . info R = len ( self . rlzs_assoc . realizations ) logging . info ( 'There are %d realization(s)' , R ) if self . oqparam . imtls : self . datastore [ 'weights' ] = arr = build_weights ( self . rlzs_assoc . realizations , self . oqparam . imt_dt ( ) ) self . datastore . set_attrs ( 'weights' , nbytes = arr . nbytes ) if hasattr ( self , 'hdf5cache' ) : with hdf5 . File ( self . hdf5cache , 'r+' ) as cache : cache [ 'weights' ] = arr if 'event_based' in self . oqparam . calculation_mode and R >= TWO16 : raise ValueError ( 'The logic tree has %d realizations, the maximum ' 'is %d' % ( R , TWO16 ) ) elif R > 10000 : logging . warning ( 'The logic tree has %d realizations(!), please consider ' 'sampling it' , R ) self . datastore . flush ( ) | Save info about the composite source model inside the csm_info dataset |
44,047 | def read_shakemap ( self , haz_sitecol , assetcol ) : oq = self . oqparam E = oq . number_of_ground_motion_fields oq . risk_imtls = oq . imtls or self . datastore . parent [ 'oqparam' ] . imtls extra = self . riskmodel . get_extra_imts ( oq . risk_imtls ) if extra : logging . warning ( 'There are risk functions for not available IMTs ' 'which will be ignored: %s' % extra ) logging . info ( 'Getting/reducing shakemap' ) with self . monitor ( 'getting/reducing shakemap' ) : smap = oq . shakemap_id if oq . shakemap_id else numpy . load ( oq . inputs [ 'shakemap' ] ) sitecol , shakemap , discarded = get_sitecol_shakemap ( smap , oq . imtls , haz_sitecol , oq . asset_hazard_distance [ 'default' ] , oq . discard_assets ) if len ( discarded ) : self . datastore [ 'discarded' ] = discarded assetcol = assetcol . reduce_also ( sitecol ) logging . info ( 'Building GMFs' ) with self . monitor ( 'building/saving GMFs' ) : imts , gmfs = to_gmfs ( shakemap , oq . spatial_correlation , oq . cross_correlation , oq . site_effects , oq . truncation_level , E , oq . random_seed , oq . imtls ) save_gmf_data ( self . datastore , sitecol , gmfs , imts ) return sitecol , assetcol | Enabled only if there is a shakemap_id parameter in the job . ini . Download unzip parse USGS shakemap files and build a corresponding set of GMFs which are then filtered with the hazard site collection and stored in the datastore . |
44,048 | def bind ( end_point , socket_type ) : sock = context . socket ( socket_type ) try : sock . bind ( end_point ) except zmq . error . ZMQError as exc : sock . close ( ) raise exc . __class__ ( '%s: %s' % ( exc , end_point ) ) return sock | Bind to a zmq URL ; raise a proper error if the URL is invalid ; return a zmq socket . |
44,049 | def send ( self , obj ) : self . zsocket . send_pyobj ( obj ) self . num_sent += 1 if self . socket_type == zmq . REQ : return self . zsocket . recv_pyobj ( ) | Send an object to the remote server ; block and return the reply if the socket type is REQ . |
44,050 | def angular_distance ( km , lat , lat2 = None ) : if lat2 is not None : lat = max ( abs ( lat ) , abs ( lat2 ) ) return km * KM_TO_DEGREES / math . cos ( lat * DEGREES_TO_RAD ) | Return the angular distance of two points at the given latitude . |
44,051 | def assoc ( objects , sitecol , assoc_dist , mode , asset_refs = ( ) ) : if isinstance ( objects , numpy . ndarray ) or hasattr ( objects , 'lons' ) : return _GeographicObjects ( objects ) . assoc ( sitecol , assoc_dist , mode ) else : return _GeographicObjects ( sitecol ) . assoc2 ( objects , assoc_dist , mode , asset_refs ) | Associate geographic objects to a site collection . |
44,052 | def line_intersects_itself ( lons , lats , closed_shape = False ) : assert len ( lons ) == len ( lats ) if len ( lons ) <= 3 : return False west , east , north , south = get_spherical_bounding_box ( lons , lats ) proj = OrthographicProjection ( west , east , north , south ) xx , yy = proj ( lons , lats ) if not shapely . geometry . LineString ( list ( zip ( xx , yy ) ) ) . is_simple : return True if closed_shape : xx , yy = proj ( numpy . roll ( lons , 1 ) , numpy . roll ( lats , 1 ) ) if not shapely . geometry . LineString ( list ( zip ( xx , yy ) ) ) . is_simple : return True return False | Return True if line of points intersects itself . Line with the last point repeating the first one considered intersecting itself . |
44,053 | def get_bounding_box ( obj , maxdist ) : if hasattr ( obj , 'get_bounding_box' ) : return obj . get_bounding_box ( maxdist ) elif hasattr ( obj , 'polygon' ) : bbox = obj . polygon . get_bbox ( ) else : if isinstance ( obj , list ) : lons = numpy . array ( [ loc . longitude for loc in obj ] ) lats = numpy . array ( [ loc . latitude for loc in obj ] ) else : lons , lats = obj [ 'lon' ] , obj [ 'lat' ] min_lon , max_lon = lons . min ( ) , lons . max ( ) if cross_idl ( min_lon , max_lon ) : lons %= 360 bbox = lons . min ( ) , lats . min ( ) , lons . max ( ) , lats . max ( ) a1 = min ( maxdist * KM_TO_DEGREES , 90 ) a2 = min ( angular_distance ( maxdist , bbox [ 1 ] , bbox [ 3 ] ) , 180 ) return bbox [ 0 ] - a2 , bbox [ 1 ] - a1 , bbox [ 2 ] + a2 , bbox [ 3 ] + a1 | Return the dilated bounding box of a geometric object . |
44,054 | def get_spherical_bounding_box ( lons , lats ) : north , south = numpy . max ( lats ) , numpy . min ( lats ) west , east = numpy . min ( lons ) , numpy . max ( lons ) assert ( - 180 <= west <= 180 ) and ( - 180 <= east <= 180 ) , ( west , east ) if get_longitudinal_extent ( west , east ) < 0 : if hasattr ( lons , 'flatten' ) : lons = lons . flatten ( ) west = min ( lon for lon in lons if lon > 0 ) east = max ( lon for lon in lons if lon < 0 ) if not all ( ( get_longitudinal_extent ( west , lon ) >= 0 and get_longitudinal_extent ( lon , east ) >= 0 ) for lon in lons ) : raise ValueError ( 'points collection has longitudinal extent ' 'wider than 180 deg' ) return SphericalBB ( west , east , north , south ) | Given a collection of points find and return the bounding box as a pair of longitudes and a pair of latitudes . |
44,055 | def get_middle_point ( lon1 , lat1 , lon2 , lat2 ) : if lon1 == lon2 and lat1 == lat2 : return lon1 , lat1 dist = geodetic . geodetic_distance ( lon1 , lat1 , lon2 , lat2 ) azimuth = geodetic . azimuth ( lon1 , lat1 , lon2 , lat2 ) return geodetic . point_at ( lon1 , lat1 , azimuth , dist / 2.0 ) | Given two points return the point exactly in the middle lying on the same great circle arc . |
44,056 | def cartesian_to_spherical ( vectors ) : rr = numpy . sqrt ( numpy . sum ( vectors * vectors , axis = - 1 ) ) xx , yy , zz = vectors . T lats = numpy . degrees ( numpy . arcsin ( ( zz / rr ) . clip ( - 1. , 1. ) ) ) lons = numpy . degrees ( numpy . arctan2 ( yy , xx ) ) depths = EARTH_RADIUS - rr return lons . T , lats . T , depths | Return the spherical coordinates for coordinates in Cartesian space . |
44,057 | def triangle_area ( e1 , e2 , e3 ) : e1_length = numpy . sqrt ( numpy . sum ( e1 * e1 , axis = - 1 ) ) e2_length = numpy . sqrt ( numpy . sum ( e2 * e2 , axis = - 1 ) ) e3_length = numpy . sqrt ( numpy . sum ( e3 * e3 , axis = - 1 ) ) s = ( e1_length + e2_length + e3_length ) / 2.0 return numpy . sqrt ( s * ( s - e1_length ) * ( s - e2_length ) * ( s - e3_length ) ) | Get the area of triangle formed by three vectors . |
44,058 | def normalized ( vector ) : length = numpy . sum ( vector * vector , axis = - 1 ) length = numpy . sqrt ( length . reshape ( length . shape + ( 1 , ) ) ) return vector / length | Get unit vector for a given one . |
44,059 | def point_to_polygon_distance ( polygon , pxx , pyy ) : pxx = numpy . array ( pxx ) pyy = numpy . array ( pyy ) assert pxx . shape == pyy . shape if pxx . ndim == 0 : pxx = pxx . reshape ( ( 1 , ) ) pyy = pyy . reshape ( ( 1 , ) ) result = numpy . array ( [ polygon . distance ( shapely . geometry . Point ( pxx . item ( i ) , pyy . item ( i ) ) ) for i in range ( pxx . size ) ] ) return result . reshape ( pxx . shape ) | Calculate the distance to polygon for each point of the collection on the 2d Cartesian plane . |
44,060 | def cross_idl ( lon1 , lon2 , * lons ) : lons = ( lon1 , lon2 ) + lons l1 , l2 = min ( lons ) , max ( lons ) return l1 * l2 < 0 and abs ( l1 - l2 ) > 180 | Return True if two longitude values define line crossing international date line . |
44,061 | def normalize_lons ( l1 , l2 ) : if l1 > l2 : l1 , l2 = l2 , l1 delta = l2 - l1 if l1 < 0 and l2 > 0 and delta > 180 : return [ ( - 180 , l1 ) , ( l2 , 180 ) ] elif l1 > 0 and l2 > 180 and delta < 180 : return [ ( l1 , 180 ) , ( - 180 , l2 - 360 ) ] elif l1 < - 180 and l2 < 0 and delta < 180 : return [ ( l1 + 360 , 180 ) , ( l2 , - 180 ) ] return [ ( l1 , l2 ) ] | An international date line safe way of returning a range of longitudes . |
44,062 | def get_closest ( self , lon , lat , depth = 0 ) : xyz = spherical_to_cartesian ( lon , lat , depth ) min_dist , idx = self . kdtree . query ( xyz ) return self . objects [ idx ] , min_dist | Get the closest object to the given longitude and latitude and its distance . |
44,063 | def assoc2 ( self , assets_by_site , assoc_dist , mode , asset_refs ) : assert mode in 'strict filter' , mode self . objects . filtered asset_dt = numpy . dtype ( [ ( 'asset_ref' , vstr ) , ( 'lon' , F32 ) , ( 'lat' , F32 ) ] ) assets_by_sid = collections . defaultdict ( list ) discarded = [ ] for assets in assets_by_site : lon , lat = assets [ 0 ] . location obj , distance = self . get_closest ( lon , lat ) if distance <= assoc_dist : assets_by_sid [ obj [ 'sids' ] ] . extend ( assets ) elif mode == 'strict' : raise SiteAssociationError ( 'There is nothing closer than %s km ' 'to site (%s %s)' % ( assoc_dist , lon , lat ) ) else : discarded . extend ( assets ) sids = sorted ( assets_by_sid ) if not sids : raise SiteAssociationError ( 'Could not associate any site to any assets within the ' 'asset_hazard_distance of %s km' % assoc_dist ) assets_by_site = [ sorted ( assets_by_sid [ sid ] , key = operator . attrgetter ( 'ordinal' ) ) for sid in sids ] data = [ ( asset_refs [ asset . ordinal ] , ) + asset . location for asset in discarded ] discarded = numpy . array ( data , asset_dt ) return self . objects . filtered ( sids ) , assets_by_site , discarded | Associated a list of assets by site to the site collection used to instantiate GeographicObjects . |
44,064 | def ffconvert ( fname , limit_states , ff , min_iml = 1E-10 ) : with context ( fname , ff ) : ffs = ff [ 1 : ] imls = ff . imls nodamage = imls . attrib . get ( 'noDamageLimit' ) if nodamage == 0 : logging . warning ( 'Found a noDamageLimit=0 in %s, line %s, ' 'using %g instead' , fname , ff . lineno , min_iml ) nodamage = min_iml with context ( fname , imls ) : attrs = dict ( format = ff [ 'format' ] , imt = imls [ 'imt' ] , id = ff [ 'id' ] , nodamage = nodamage ) LS = len ( limit_states ) if LS != len ( ffs ) : with context ( fname , ff ) : raise InvalidFile ( 'expected %d limit states, found %d' % ( LS , len ( ffs ) ) ) if ff [ 'format' ] == 'continuous' : minIML = float ( imls [ 'minIML' ] ) if minIML == 0 : logging . warning ( 'Found minIML=0 in %s, line %s, using %g instead' , fname , imls . lineno , min_iml ) minIML = min_iml attrs [ 'minIML' ] = minIML attrs [ 'maxIML' ] = float ( imls [ 'maxIML' ] ) array = numpy . zeros ( LS , [ ( 'mean' , F64 ) , ( 'stddev' , F64 ) ] ) for i , ls , node in zip ( range ( LS ) , limit_states , ff [ 1 : ] ) : if ls != node [ 'ls' ] : with context ( fname , node ) : raise InvalidFile ( 'expected %s, found' % ( ls , node [ 'ls' ] ) ) array [ 'mean' ] [ i ] = node [ 'mean' ] array [ 'stddev' ] [ i ] = node [ 'stddev' ] elif ff [ 'format' ] == 'discrete' : attrs [ 'imls' ] = ~ imls valid . check_levels ( attrs [ 'imls' ] , attrs [ 'imt' ] , min_iml ) num_poes = len ( attrs [ 'imls' ] ) array = numpy . zeros ( ( LS , num_poes ) ) for i , ls , node in zip ( range ( LS ) , limit_states , ff [ 1 : ] ) : with context ( fname , node ) : if ls != node [ 'ls' ] : raise InvalidFile ( 'expected %s, found' % ( ls , node [ 'ls' ] ) ) poes = ( ~ node if isinstance ( ~ node , list ) else valid . probabilities ( ~ node ) ) if len ( poes ) != num_poes : raise InvalidFile ( 'expected %s, found' % ( num_poes , len ( poes ) ) ) array [ i , : ] = poes return array , attrs | Convert a fragility function into a numpy array plus a bunch of attributes . |
44,065 | def taxonomy ( value ) : try : value . encode ( 'ascii' ) except UnicodeEncodeError : raise ValueError ( 'tag %r is not ASCII' % value ) if re . search ( r'\s' , value ) : raise ValueError ( 'The taxonomy %r contains whitespace chars' % value ) return value | Any ASCII character goes into a taxonomy except spaces . |
44,066 | def update_validators ( ) : validators . update ( { 'fragilityFunction.id' : valid . utf8 , 'vulnerabilityFunction.id' : valid . utf8 , 'consequenceFunction.id' : valid . utf8 , 'asset.id' : valid . asset_id , 'costType.name' : valid . cost_type , 'costType.type' : valid . cost_type_type , 'cost.type' : valid . cost_type , 'area.type' : valid . name , 'isAbsolute' : valid . boolean , 'insuranceLimit' : valid . positivefloat , 'deductible' : valid . positivefloat , 'occupants' : valid . positivefloat , 'value' : valid . positivefloat , 'retrofitted' : valid . positivefloat , 'number' : valid . compose ( valid . positivefloat , valid . nonzero ) , 'vulnerabilitySetID' : str , 'vulnerabilityFunctionID' : str , 'lossCategory' : valid . utf8 , 'lr' : valid . probability , 'lossRatio' : valid . positivefloats , 'coefficientsVariation' : valid . positivefloats , 'probabilisticDistribution' : valid . Choice ( 'LN' , 'BT' ) , 'dist' : valid . Choice ( 'LN' , 'BT' , 'PM' ) , 'meanLRs' : valid . positivefloats , 'covLRs' : valid . positivefloats , 'format' : valid . ChoiceCI ( 'discrete' , 'continuous' ) , 'mean' : valid . positivefloat , 'stddev' : valid . positivefloat , 'minIML' : valid . positivefloat , 'maxIML' : valid . positivefloat , 'limitStates' : valid . namelist , 'noDamageLimit' : valid . NoneOr ( valid . positivefloat ) , 'loss_type' : valid_loss_types , 'losses' : valid . positivefloats , 'averageLoss' : valid . positivefloat , 'stdDevLoss' : valid . positivefloat , 'ffs.type' : valid . ChoiceCI ( 'lognormal' ) , 'assetLifeExpectancy' : valid . positivefloat , 'interestRate' : valid . positivefloat , 'lossType' : valid_loss_types , 'aalOrig' : valid . positivefloat , 'aalRetr' : valid . positivefloat , 'ratio' : valid . positivefloat , 'cf' : asset_mean_stddev , 'damage' : damage_triple , 'damageStates' : valid . namelist , 'taxonomy' : taxonomy , 'tagNames' : valid . namelist , } ) | Call this to updade the global nrml . validators |
44,067 | def barray ( iterlines ) : lst = [ line . encode ( 'utf-8' ) for line in iterlines ] arr = numpy . array ( lst ) return arr | Array of bytes |
44,068 | def losses_by_tag ( dstore , tag ) : dt = [ ( tag , vstr ) ] + dstore [ 'oqparam' ] . loss_dt_list ( ) aids = dstore [ 'assetcol/array' ] [ tag ] dset , stats = _get ( dstore , 'avg_losses' ) arr = dset . value tagvalues = dstore [ 'assetcol/tagcol/' + tag ] [ 1 : ] for s , stat in enumerate ( stats ) : out = numpy . zeros ( len ( tagvalues ) , dt ) for li , ( lt , lt_dt ) in enumerate ( dt [ 1 : ] ) : for i , tagvalue in enumerate ( tagvalues ) : out [ i ] [ tag ] = tagvalue counts = arr [ aids == i + 1 , s , li ] . sum ( ) if counts : out [ i ] [ lt ] = counts yield stat , out | Statistical average losses by tag . For instance call |
44,069 | def dump ( self , fname ) : url = '%s/v1/calc/%d/datastore' % ( self . server , self . calc_id ) resp = self . sess . get ( url , stream = True ) down = 0 with open ( fname , 'wb' ) as f : logging . info ( 'Saving %s' , fname ) for chunk in resp . iter_content ( CHUNKSIZE ) : f . write ( chunk ) down += len ( chunk ) println ( 'Downloaded {:,} bytes' . format ( down ) ) print ( ) | Dump the remote datastore on a local path . |
44,070 | def _compute_small_mag_correction_term ( C , mag , rhypo ) : if mag >= 3.00 and mag < 5.5 : min_term = np . minimum ( rhypo , C [ 'Rm' ] ) max_term = np . maximum ( min_term , 10 ) term_ln = np . log ( max_term / 20 ) term_ratio = ( ( 5.50 - mag ) / C [ 'a1' ] ) temp = ( term_ratio ) ** C [ 'a2' ] * ( C [ 'b1' ] + C [ 'b2' ] * term_ln ) return 1 / np . exp ( temp ) else : return 1 | small magnitude correction applied to the median values |
44,071 | def _apply_adjustments ( COEFFS , C_ADJ , tau_ss , mean , stddevs , sites , rup , dists , imt , stddev_types , log_phi_ss , NL = None , tau_value = None ) : c1_dists = _compute_C1_term ( C_ADJ , dists ) phi_ss = _compute_phi_ss ( C_ADJ , rup . mag , c1_dists , log_phi_ss , C_ADJ [ 'mean_phi_ss' ] ) mean_corr = np . exp ( mean ) * C_ADJ [ 'k_adj' ] * _compute_small_mag_correction_term ( C_ADJ , rup . mag , dists ) mean_corr = np . log ( mean_corr ) std_corr = _get_corr_stddevs ( COEFFS [ imt ] , tau_ss , stddev_types , len ( sites . vs30 ) , phi_ss , NL , tau_value ) stddevs = np . array ( std_corr ) return mean_corr , stddevs | This method applies adjustments to the mean and standard deviation . The small - magnitude adjustments are applied to mean whereas the embeded single station sigma logic tree is applied to the total standard deviation . |
44,072 | def get_info ( self , sm_id ) : sm = self . source_models [ sm_id ] num_samples = sm . samples if self . num_samples else 0 return self . __class__ ( self . gsim_lt , self . seed , num_samples , [ sm ] , self . tot_weight ) | Extract a CompositionInfo instance containing the single model of index sm_id . |
44,073 | def get_source_model ( self , src_group_id ) : for smodel in self . source_models : for src_group in smodel . src_groups : if src_group . id == src_group_id : return smodel | Return the source model for the given src_group_id |
44,074 | def get_model ( self , sm_id ) : sm = self . source_models [ sm_id ] if self . source_model_lt . num_samples : self . source_model_lt . num_samples = sm . samples new = self . __class__ ( self . gsim_lt , self . source_model_lt , [ sm ] , self . optimize_same_id ) new . sm_id = sm_id return new | Extract a CompositeSourceModel instance containing the single model of index sm_id . |
44,075 | def new ( self , sources_by_grp ) : source_models = [ ] for sm in self . source_models : src_groups = [ ] for src_group in sm . src_groups : sg = copy . copy ( src_group ) sg . sources = sorted ( sources_by_grp . get ( sg . id , [ ] ) , key = operator . attrgetter ( 'id' ) ) src_groups . append ( sg ) newsm = logictree . LtSourceModel ( sm . names , sm . weight , sm . path , src_groups , sm . num_gsim_paths , sm . ordinal , sm . samples ) source_models . append ( newsm ) new = self . __class__ ( self . gsim_lt , self . source_model_lt , source_models , self . optimize_same_id ) new . info . update_eff_ruptures ( new . get_num_ruptures ( ) ) new . info . tot_weight = new . get_weight ( ) return new | Generate a new CompositeSourceModel from the given dictionary . |
44,076 | def check_dupl_sources ( self ) : dd = collections . defaultdict ( list ) for src_group in self . src_groups : for src in src_group : try : srcid = src . source_id except AttributeError : srcid = src [ 'id' ] dd [ srcid ] . append ( src ) dupl = [ ] for srcid , srcs in sorted ( dd . items ( ) ) : if len ( srcs ) > 1 : _assert_equal_sources ( srcs ) dupl . append ( srcs ) return dupl | Extracts duplicated sources i . e . sources with the same source_id in different source groups . Raise an exception if there are sources with the same ID which are not duplicated . |
44,077 | def get_sources ( self , kind = 'all' ) : assert kind in ( 'all' , 'indep' , 'mutex' ) , kind sources = [ ] for sm in self . source_models : for src_group in sm . src_groups : if kind in ( 'all' , src_group . src_interdep ) : for src in src_group : if sm . samples > 1 : src . samples = sm . samples sources . append ( src ) return sources | Extract the sources contained in the source models by optionally filtering and splitting them depending on the passed parameter . |
44,078 | def init_serials ( self , ses_seed ) : sources = self . get_sources ( ) serial = ses_seed for src in sources : nr = src . num_ruptures src . serial = serial serial += nr | Generate unique seeds for each rupture with numpy . arange . This should be called only in event based calculators |
44,079 | def get_maxweight ( self , weight , concurrent_tasks , minweight = MINWEIGHT ) : totweight = self . get_weight ( weight ) ct = concurrent_tasks or 1 mw = math . ceil ( totweight / ct ) return max ( mw , minweight ) | Return an appropriate maxweight for use in the block_splitter |
44,080 | def weight_list_to_tuple ( data , attr_name ) : if len ( data [ 'Value' ] ) != len ( data [ 'Weight' ] ) : raise ValueError ( 'Number of weights do not correspond to number of ' 'attributes in %s' % attr_name ) weight = np . array ( data [ 'Weight' ] ) if fabs ( np . sum ( weight ) - 1. ) > 1E-7 : raise ValueError ( 'Weights do not sum to 1.0 in %s' % attr_name ) data_tuple = [ ] for iloc , value in enumerate ( data [ 'Value' ] ) : data_tuple . append ( ( value , weight [ iloc ] ) ) return data_tuple | Converts a list of values and corresponding weights to a tuple of values |
44,081 | def parse_tect_region_dict_to_tuples ( region_dict ) : output_region_dict = [ ] tuple_keys = [ 'Displacement_Length_Ratio' , 'Shear_Modulus' ] for region in region_dict : for val_name in tuple_keys : region [ val_name ] = weight_list_to_tuple ( region [ val_name ] , val_name ) region [ 'Magnitude_Scaling_Relation' ] = weight_list_to_tuple ( region [ 'Magnitude_Scaling_Relation' ] , 'Magnitude Scaling Relation' ) output_region_dict . append ( region ) return output_region_dict | Parses the tectonic regionalisation dictionary attributes to tuples |
44,082 | def get_scaling_relation_tuple ( msr_dict ) : for iloc , value in enumerate ( msr_dict [ 'Value' ] ) : if not value in SCALE_REL_MAP . keys ( ) : raise ValueError ( 'Scaling relation %s not supported!' % value ) msr_dict [ 'Value' ] [ iloc ] = SCALE_REL_MAP [ value ] ( ) return weight_list_to_tuple ( msr_dict , 'Magnitude Scaling Relation' ) | For a dictionary of scaling relation values convert string list to object list and then to tuple |
44,083 | def read_file ( self , mesh_spacing = 1.0 ) : tectonic_reg = self . process_tectonic_regionalisation ( ) model = mtkActiveFaultModel ( self . data [ 'Fault_Model_ID' ] , self . data [ 'Fault_Model_Name' ] ) for fault in self . data [ 'Fault_Model' ] : fault_geometry = self . read_fault_geometry ( fault [ 'Fault_Geometry' ] , mesh_spacing ) if fault [ 'Shear_Modulus' ] : fault [ 'Shear_Modulus' ] = weight_list_to_tuple ( fault [ 'Shear_Modulus' ] , '%s Shear Modulus' % fault [ 'ID' ] ) if fault [ 'Displacement_Length_Ratio' ] : fault [ 'Displacement_Length_Ratio' ] = weight_list_to_tuple ( fault [ 'Displacement_Length_Ratio' ] , '%s Displacement to Length Ratio' % fault [ 'ID' ] ) fault_source = mtkActiveFault ( fault [ 'ID' ] , fault [ 'Fault_Name' ] , fault_geometry , weight_list_to_tuple ( fault [ 'Slip' ] , '%s - Slip' % fault [ 'ID' ] ) , float ( fault [ 'Rake' ] ) , fault [ 'Tectonic_Region' ] , float ( fault [ 'Aseismic' ] ) , weight_list_to_tuple ( fault [ 'Scaling_Relation_Sigma' ] , '%s Scaling_Relation_Sigma' % fault [ 'ID' ] ) , neotectonic_fault = None , scale_rel = get_scaling_relation_tuple ( fault [ 'Magnitude_Scaling_Relation' ] ) , aspect_ratio = fault [ 'Aspect_Ratio' ] , shear_modulus = fault [ 'Shear_Modulus' ] , disp_length_ratio = fault [ 'Displacement_Length_Ratio' ] ) if tectonic_reg : fault_source . get_tectonic_regionalisation ( tectonic_reg , fault [ 'Tectonic_Region' ] ) assert isinstance ( fault [ 'MFD_Model' ] , list ) fault_source . generate_config_set ( fault [ 'MFD_Model' ] ) model . faults . append ( fault_source ) return model , tectonic_reg | Reads the file and returns an instance of the FaultSource class . |
44,084 | def process_tectonic_regionalisation ( self ) : if 'tectonic_regionalisation' in self . data . keys ( ) : tectonic_reg = TectonicRegionalisation ( ) tectonic_reg . populate_regions ( parse_tect_region_dict_to_tuples ( self . data [ 'tectonic_regionalisation' ] ) ) else : tectonic_reg = None return tectonic_reg | Processes the tectonic regionalisation from the yaml file |
44,085 | def read_fault_geometry ( self , geo_dict , mesh_spacing = 1.0 ) : if geo_dict [ 'Fault_Typology' ] == 'Simple' : raw_trace = geo_dict [ 'Fault_Trace' ] trace = Line ( [ Point ( raw_trace [ ival ] , raw_trace [ ival + 1 ] ) for ival in range ( 0 , len ( raw_trace ) , 2 ) ] ) geometry = SimpleFaultGeometry ( trace , geo_dict [ 'Dip' ] , geo_dict [ 'Upper_Depth' ] , geo_dict [ 'Lower_Depth' ] , mesh_spacing ) elif geo_dict [ 'Fault_Typology' ] == 'Complex' : trace = [ ] for raw_trace in geo_dict [ 'Fault_Trace' ] : fault_edge = Line ( [ Point ( raw_trace [ ival ] , raw_trace [ ival + 1 ] , raw_trace [ ival + 2 ] ) for ival in range ( 0 , len ( raw_trace ) , 3 ) ] ) trace . append ( fault_edge ) geometry = ComplexFaultGeometry ( trace , mesh_spacing ) else : raise ValueError ( 'Unrecognised or unsupported fault geometry!' ) return geometry | Creates the fault geometry from the parameters specified in the dictionary . |
44,086 | def _get_distance_scaling_term ( self , C , mag , rrup ) : return ( C [ "r1" ] + C [ "r2" ] * mag ) * np . log10 ( rrup + C [ "r3" ] ) | Returns the distance scaling parameter |
44,087 | def _get_style_of_faulting_term ( self , C , rake ) : if rake > - 150.0 and rake <= - 30.0 : return C [ 'fN' ] elif rake > 30.0 and rake <= 150.0 : return C [ 'fR' ] else : return C [ 'fSS' ] | Returns the style of faulting term . Cauzzi et al . determind SOF from the plunge of the B - T - and P - axes . For consistency with existing GMPEs the Wells & Coppersmith model is preferred |
44,088 | def _get_site_amplification_term ( self , C , vs30 ) : s_b , s_c , s_d = self . _get_site_dummy_variables ( vs30 ) return ( C [ "sB" ] * s_b ) + ( C [ "sC" ] * s_c ) + ( C [ "sD" ] * s_d ) | Returns the site amplification term on the basis of Eurocode 8 site class |
44,089 | def _get_site_dummy_variables ( self , vs30 ) : s_b = np . zeros_like ( vs30 ) s_c = np . zeros_like ( vs30 ) s_d = np . zeros_like ( vs30 ) s_b [ np . logical_and ( vs30 >= 360. , vs30 < 800. ) ] = 1.0 s_c [ np . logical_and ( vs30 >= 180. , vs30 < 360. ) ] = 1.0 s_d [ vs30 < 180 ] = 1.0 return s_b , s_c , s_d | Returns the Eurocode 8 site class dummy variable |
44,090 | def get_recurrence ( self , config ) : model = MFD_MAP [ config [ 'Model_Name' ] ] ( ) model . setUp ( config ) model . get_mmax ( config , self . msr , self . rake , self . area ) model . mmax = model . mmax + ( self . msr_sigma * model . mmax_sigma ) if 'AndersonLucoAreaMmax' in config [ 'Model_Name' ] : if not self . disp_length_ratio : self . disp_length_ratio = 1.25E-5 min_mag , bin_width , occur_rates = model . get_mfd ( self . slip , self . area , self . shear_modulus , self . disp_length_ratio ) else : min_mag , bin_width , occur_rates = model . get_mfd ( self . slip , self . area , self . shear_modulus ) self . recurrence = IncrementalMFD ( min_mag , bin_width , occur_rates ) self . magnitudes = min_mag + np . cumsum ( bin_width * np . ones ( len ( occur_rates ) , dtype = float ) ) - bin_width self . max_mag = np . max ( self . magnitudes ) | Calculates the recurrence model for the given settings as an instance of the openquake . hmtk . models . IncrementalMFD |
44,091 | def get_tectonic_regionalisation ( self , regionalisation , region_type = None ) : if region_type : self . trt = region_type if not self . trt in regionalisation . key_list : raise ValueError ( 'Tectonic region classification missing or ' 'not defined in regionalisation' ) for iloc , key_val in enumerate ( regionalisation . key_list ) : if self . trt in key_val : self . regionalisation = regionalisation . regionalisation [ iloc ] if not self . shear_modulus : self . shear_modulus = self . regionalisation . shear_modulus if not self . msr : self . msr = self . regionalisation . scaling_rel if not self . disp_length_ratio : self . disp_length_ratio = self . regionalisation . disp_length_ratio break return | Defines the tectonic region and updates the shear modulus magnitude scaling relation and displacement to length ratio using the regional values if not previously defined for the fault |
44,092 | def select_catalogue ( self , selector , distance , distance_metric = "rupture" , upper_eq_depth = None , lower_eq_depth = None ) : if selector . catalogue . get_number_events ( ) < 1 : raise ValueError ( 'No events found in catalogue!' ) if ( 'rupture' in distance_metric ) : self . catalogue = selector . within_rupture_distance ( self . geometry . surface , distance , upper_depth = upper_eq_depth , lower_depth = lower_eq_depth ) else : self . catalogue = selector . within_joyner_boore_distance ( self . geometry . surface , distance , upper_depth = upper_eq_depth , lower_depth = lower_eq_depth ) | Select earthquakes within a specied distance of the fault |
44,093 | def generate_config_set ( self , config ) : if isinstance ( config , dict ) : self . config = [ ( config , 1.0 ) ] elif isinstance ( config , list ) : total_weight = 0. self . config = [ ] for params in config : weight = params [ 'Model_Weight' ] total_weight += params [ 'Model_Weight' ] self . config . append ( ( params , weight ) ) if fabs ( total_weight - 1.0 ) > 1E-7 : raise ValueError ( 'MFD config weights do not sum to 1.0 for ' 'fault %s' % self . id ) else : raise ValueError ( 'MFD config must be input as dictionary or list!' ) | Generates a list of magnitude frequency distributions and renders as a tuple |
44,094 | def collapse_branches ( self , mmin , bin_width , mmax ) : master_mags = np . arange ( mmin , mmax + ( bin_width / 2. ) , bin_width ) master_rates = np . zeros ( len ( master_mags ) , dtype = float ) for model in self . mfd_models : id0 = np . logical_and ( master_mags >= np . min ( model . magnitudes ) - 1E-9 , master_mags <= np . max ( model . magnitudes ) + 1E-9 ) yvals = np . log10 ( model . recurrence . occur_rates ) interp_y = np . interp ( master_mags [ id0 ] , model . magnitudes , yvals ) master_rates [ id0 ] = master_rates [ id0 ] + ( model . weight * 10. ** interp_y ) return IncrementalMFD ( mmin , bin_width , master_rates ) | Collapse the logic tree branches into a single IncrementalMFD |
44,095 | def generate_fault_source_model ( self ) : source_model = [ ] model_weight = [ ] for iloc in range ( 0 , self . get_number_mfd_models ( ) ) : model_mfd = EvenlyDiscretizedMFD ( self . mfd [ 0 ] [ iloc ] . min_mag , self . mfd [ 0 ] [ iloc ] . bin_width , self . mfd [ 0 ] [ iloc ] . occur_rates . tolist ( ) ) if isinstance ( self . geometry , ComplexFaultGeometry ) : source = mtkComplexFaultSource ( self . id , self . name , self . trt , self . geometry . surface , self . mfd [ 2 ] [ iloc ] , self . rupt_aspect_ratio , model_mfd , self . rake ) source . fault_edges = self . geometry . trace else : source = mtkSimpleFaultSource ( self . id , self . name , self . trt , self . geometry . surface , self . geometry . dip , self . geometry . upper_depth , self . geometry . lower_depth , self . mfd [ 2 ] [ iloc ] , self . rupt_aspect_ratio , model_mfd , self . rake ) source . fault_trace = self . geometry . trace source_model . append ( source ) model_weight . append ( self . mfd [ 1 ] [ iloc ] ) return source_model , model_weight | Creates a resulting openquake . hmtk fault source set . |
44,096 | def attrib ( self ) : return dict ( [ ( 'id' , str ( self . id ) ) , ( 'name' , str ( self . name ) ) , ( 'tectonicRegion' , str ( self . trt ) ) , ] ) | General XML element attributes for a seismic source as a dict . |
44,097 | def attrib ( self ) : return dict ( [ ( 'aValue' , str ( self . a_val ) ) , ( 'bValue' , str ( self . b_val ) ) , ( 'minMag' , str ( self . min_mag ) ) , ( 'maxMag' , str ( self . max_mag ) ) , ] ) | An dict of XML element attributes for this MFD . |
44,098 | def attrib ( self ) : return dict ( [ ( 'probability' , str ( self . probability ) ) , ( 'strike' , str ( self . strike ) ) , ( 'dip' , str ( self . dip ) ) , ( 'rake' , str ( self . rake ) ) , ] ) | A dict of XML element attributes for this NodalPlane . |
44,099 | def jbcorrelation ( sites_or_distances , imt , vs30_clustering = False ) : if hasattr ( sites_or_distances , 'mesh' ) : distances = sites_or_distances . mesh . get_distance_matrix ( ) else : distances = sites_or_distances if imt . period < 1 : if not vs30_clustering : b = 8.5 + 17.2 * imt . period else : b = 40.7 - 15.0 * imt . period else : b = 22.0 + 3.7 * imt . period return numpy . exp ( ( - 3.0 / b ) * distances ) | Returns the Jayaram - Baker correlation model . |
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