''' ''' # pylint: disable=E1103 # pylint: disable=no-name-in-module import numpy as np import scipy.spatial as sp from netCDF4 import Dataset import copy # DEBUG ONLY- allows multiple runs without corruption from pynemo import nemo_bdy_grid_angle from pynemo.nemo_bdy_lib import rot_rep " import foo method_to_call = getattr(foo, 'bar') result = method_to_call() You could shorten lines 2 and 3 to: result = getattr(foo, 'bar')() " class Extract: def __init__(self, setup, DstCoord, Grid): self.g_type = Grid.grid_type DC = copy.deepcopy(DstCoord) dst_lon = DC.bdy_lonlat[self.g_type]['lon'][Grid.bdy_r == 0] dst_lat = DC.bdy_lonlat[self.g_type]['lat'][Grid.bdy_r == 0] self.dst_dep = DC.depths[self.g_type]['bdy_hbat'][Grid.bdy_r == 0] self.harm_Im = {} # tidal boundary data: Imaginary self.harm_Re = {} # tidal boundary data: Real # Modify lon for 0-360 TODO this needs to be auto-dectected dst_lon = np.array([x if x > 0 else x+360 for x in dst_lon]) fileIDb = '../data/tide/grid_tpxo7.2.nc' # TPX bathymetry file nb = Dataset(fileIDb) # Open the TPX bathybetry file using the NetCDF4-Python library # Open the TPX Datafiles using the NetCDF4-Python library if self.g_type == 't': self.fileID = '../data/tide/h_tpxo7.2.nc' # TPX sea surface height file self.var_Im = 'hIm' self.var_Re = 'hRe' elif (self.g_type == 'u') or (self.g_type == 'v') : self.fileID = '../data/tide/u_tpxo7.2.nc' # TPX velocity file self.var_Im = 'UIm' self.var_Re = 'URe' self.var_Im2 = 'VIm' self.var_Re2 = 'VRe' self.key_tr = setup['trans'] # Determine the grid angle for rotating vector qunatities maxJ = DC.lonlat['t']['lon'].shape[0] maxI = DC.lonlat['t']['lon'].shape[1] GridAngles = nemo_bdy_grid_angle.GridAngle(setup['dst_hgr'], 1, maxI, 1, maxJ, self.g_type) dst_gcos = np.ones([maxJ, maxI]) dst_gsin = np.zeros([maxJ,maxI]) dst_gcos[1:,1:] = GridAngles.cosval dst_gsin[1:,1:] = GridAngles.sinval # Retain only boundary points rotation information self.gcos = np.zeros(Grid.bdy_i.shape[0]) self.gsin = np.zeros(Grid.bdy_i.shape[0]) for p in range(Grid.bdy_i.shape[0]): self.gcos[p] = dst_gcos[Grid.bdy_i[p,1], Grid.bdy_i[p,0]] self.gsin[p] = dst_gsin[Grid.bdy_i[p,1], Grid.bdy_i[p,0]] if self.g_type == 'u': self.rot_dir = 'i' elif self.g_type == 'v': self.rot_dir = 'j' else: print 'You should not see this message!' # We will average velocities onto the T grid as there is a rotation to be done # Also need to account for east-west wrap-around nc = Dataset('../data/tide/h_tpxo7.2.nc') lon = np.ravel(np.concatenate([nc.variables['lon_z'][-2:,:], nc.variables['lon_z'][:,:], nc.variables['lon_z'][0:2,:]])) lat = np.ravel(np.concatenate([nc.variables['lat_z'][-2:,:], nc.variables['lat_z'][:,:], nc.variables['lat_z'][0:2,:]])) bat = np.ravel(np.concatenate([nb.variables['hz'][-2:,:], nb.variables['hz'][:,:], nb.variables['hz'][0:2,:]])) msk = np.ravel(np.concatenate([nb.variables['mz'][-2:,:], nb.variables['mz'][:,:], nb.variables['mz'][0:2,:]])) # Pull out the constituents that are avaibable self.cons = [] for ncon in range(nc.variables['con'].shape[0]): self.cons.append(nc.variables['con'][ncon,:].tostring().strip()) nc.close() # Close Datafile nb.close() # Close Bathymetry file # Find nearest neighbours on the source grid to each dst bdy point source_tree = sp.cKDTree(zip(lon, lat)) dst_pts = zip(dst_lon, dst_lat) # Upper bound set at 0.5 deg as the TPXO7.2 data are at 0.25 deg resolution and # we don't want to grab points from further afield nn_dist, self.nn_id = source_tree.query(dst_pts, k=4, eps=0, p=2, distance_upper_bound=0.5) # Create a weighting index for interpolation onto dst bdy point # need to check for missing values ind = nn_dist == np.inf self.nn_id[ind] = 0 # better way of carrying None in the indices? dx = (lon[self.nn_id] - np.repeat(np.reshape(dst_lon,[dst_lon.size, 1]),4,axis=1) ) * np.cos(np.repeat(np.reshape(dst_lat,[dst_lat.size, 1]),4,axis=1) * np.pi / 180.) dy = lat[self.nn_id] - np.repeat(np.reshape(dst_lat,[dst_lat.size, 1]),4,axis=1) dist_tot = np.power((np.power(dx, 2) + np.power(dy, 2)), 0.5) self.msk = msk[self.nn_id] self.bat = bat[self.nn_id] dist_tot[ind | self.msk] = np.nan dist_wei = 1/( np.divide(dist_tot,(np.repeat(np.reshape(np.nansum(dist_tot,axis=1),[dst_lat.size, 1]),4,axis=1)) ) ) self.nn_wei = dist_wei/np.repeat(np.reshape(np.nansum(dist_wei, axis=1),[dst_lat.size, 1]),4,axis=1) self.nn_wei[ind | self.msk] = 0. # Need to identify missing points and throw a warning and set values to zero mv = np.sum(self.nn_wei,axis=1) == 0 if np.sum(mv) > 1: print '##WARNING## There are', np.sum(mv), 'missing values, these will be set to ZERO' else: print '##WARNING## There is', np.sum(mv), 'missing value, this will be set to ZERO' def extract_con(self, con): if con in self.cons: con_ind = self.cons.index(con) # Extract the complex amplitude components nc = Dataset(self.fileID) # pass variable ids to nc if self.g_type == 't': vIm = np.ravel(np.concatenate([nc.variables[self.var_Im][con_ind,-2:,:], nc.variables[self.var_Im][con_ind,:,:], nc.variables[self.var_Im][con_ind,0:2,:]])) vRe = np.ravel(np.concatenate([nc.variables[self.var_Re][con_ind,-2:,:], nc.variables[self.var_Re][con_ind,:,:], nc.variables[self.var_Re][con_ind,0:2,:]])) self.harm_Im[con] = np.sum(vIm[self.nn_id]*self.nn_wei,axis=1) self.harm_Re[con] = np.sum(vRe[self.nn_id]*self.nn_wei,axis=1) else: uIm = np.concatenate([nc.variables[self.var_Im][con_ind,-2:,:], nc.variables[self.var_Im][con_ind,:,:], nc.variables[self.var_Im][con_ind,0:3,:]]) uRe = np.concatenate([nc.variables[self.var_Re][con_ind,-2:,:], nc.variables[self.var_Re][con_ind,:,:], nc.variables[self.var_Re][con_ind,0:3,:]]) vIm = np.concatenate([nc.variables[self.var_Im2][con_ind,-2:,:], nc.variables[self.var_Im2][con_ind,:,:], nc.variables[self.var_Im2][con_ind,0:2,:]]) vRe = np.concatenate([nc.variables[self.var_Re2][con_ind,-2:,:], nc.variables[self.var_Re2][con_ind,:,:], nc.variables[self.var_Re2][con_ind,0:2,:]]) # Deal with north pole. NB in TPXO7.2 data U and Z at 90N have different # values for each Longitude value! Plus there's something odd with the # hu and hv depths not being the min of surrounding T-grid depths # TODO remove hardwired 722 index point and make generic vIm = np.concatenate([vIm[:,:], np.concatenate([vIm[722:,-1],vIm[:722,-1]])[:,np.newaxis]],axis=1) vRe = np.concatenate([vRe[:,:], np.concatenate([vRe[722:,-1],vRe[:722,-1]])[:,np.newaxis]],axis=1) # Average U and V onto the T-grid uIm = np.ravel((uIm[:-1,:] + uIm[1:,:])/2) uRe = np.ravel((uRe[:-1,:] + uRe[1:,:])/2) vIm = np.ravel((vIm[:,:-1] + vIm[:,1:])/2) vRe = np.ravel((vRe[:,:-1] + vRe[:,1:])/2) if self.key_tr: # We convert to velocity using tidal model bathymetry harm_Im = np.sum(uIm[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1) harm_Re = np.sum(uRe[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1) harm_Im2 = np.sum(vIm[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1) harm_Re2 = np.sum(vRe[self.nn_id]*self.nn_wei,axis=1)/np.sum(self.bat*self.nn_wei,axis=1) else: # We convert to velocity using the regional model bathymetry harm_Im = np.sum(uIm[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep harm_Re = np.sum(uRe[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep harm_Im2 = np.sum(vIm[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep harm_Re2 = np.sum(vRe[self.nn_id]*self.nn_wei,axis=1)/self.dst_dep # Rotate vectors self.harm_Im[con] = rot_rep(harm_Im, harm_Im2, self.g_type, 'en to %s' %self.rot_dir, self.gcos, self.gsin) self.harm_Re[con] = rot_rep(harm_Re, harm_Re2, self.g_type, 'en to %s' %self.rot_dir, self.gcos, self.gsin) self.harm_Im[con][self.msk]=0. self.harm_Re[con][self.msk]=0. nc.close() else: # throw some warning print '##WARNING## Missing constituent values will be set to ZERO' self.harm_Im[con] = np.zeros(self.nn_id[:,0].size) self.harm_Re[con] = np.zeros(self.nn_id[:,0].size)