def make_blk_wrf(wrfpath, grd, bulk, date0=False, date1=False, **kargs): ''' see make_blk_interim ''' quiet = kargs.get('quiet', 0) create = kargs.get('create', 1) model = kargs.get('model', 'roms') # or roms-agrif wrffiles = kargs.get('wrffiles', 'wrfout*') dt = kargs.get('dt', 6) proj = kargs.get('proj', 'auto') data = load_blkdata_wrf(wrfpath, wrffiles, date0, date1, quiet) if not len(data): return g = roms.Grid(grd) if proj == 'auto': kargs['proj'] = g.get_projection() q = gennc.GenBlk(bulk, grd, **kargs) if create: # about original data, run data2romsblk once to test for x_original: tmp = data2romsblk(data[list(data.keys())[0]], g, **kargs) if 'x_original' in tmp: original = tmp['x_original'].shape else: original = False q.create(model, original) for d in data: # be sure time increases. Note that in load_blkdata_wrf # we checked if time increases in the dataset... not if dates are higher # then previous dates in file ntimes = netcdf.fdim(bulk, 'time') if ntimes: tin = netcdf.nctime(bulk, 'time') if tin.size and (d - tin[-1]) < datetime.timedelta(hours=dt - 0.1): print('-> not including %s' % d.isoformat()) continue if model == 'roms': if not quiet: print(' converting units:'), conv_units(data[d], model, quiet) D = data2romsblk(data[d], g, **kargs) D['date'] = d if not quiet: print(' =>filling date=%s' % d.isoformat(' ')) q.fill(D, quiet=quiet)
def make_blk_interim(interimpath, grd, bulk, date0=False, date1=False, **kargs): ''' kargs: tunits, ex: 'seconds since yyyy-mm-dd' quiet create model, 'roms' or 'roms-agrif' title keeporiginal, save original data in nc file margin, for original data to be saved) proj, use a map projection for the interpolations (grid default proj is used if auto) attr, additional global arguments, ex: attr={'some_info': 'abc','what': 123} past, use True for old data server data, default False ... ''' quiet = kargs.get('quiet', 0) create = kargs.get('create', 1) model = kargs.get('model', 'roms') # or roms-agrif past = kargs.get('past', False) # use True for old data server data proj = kargs.get('proj', 'auto') data = load_blkdata_interim(interimpath, date0, date1, quiet, past) g = roms.Grid(grd) if proj == 'auto': kargs['proj'] = g.get_projection() # about original data, run data2romsblk once to test for x_original: tmp = data2romsblk(data[list(data.keys())[0]], g, **kargs) if 'x_original' in tmp: original = tmp['x_original'].shape else: original = False q = gennc.GenBlk(bulk, grd, **kargs) if create: q.create(model, original) for d in data: if model == 'roms': if not quiet: print(' converting units:'), conv_units(data[d], model, quiet) D = data2romsblk(data[d], g, **kargs) D['date'] = d if not quiet: print(' =>filling date=%s' % d.isoformat(' ')) q.fill(D, quiet=quiet)
def make_blk_narr(grd, bulk, date0, date1=False, **kargs): ''' see make_blk_interim ps: to not include original data in file, use kargg keepor=False ''' quiet = kargs.get('quiet', 0) create = kargs.get('create', 1) model = kargs.get('model', 'roms') # or roms-agrif proj = kargs.get('proj', 'auto') data, miss = load_blkdata_narr(date0, date1, quiet=quiet) g = roms.Grid(grd) if proj == 'auto': kargs['proj'] = g.get_projection() # about original data, run data2romsblk once to test for x_original: tmp = data2romsblk(data[list(data.keys())[0]], g, **kargs) if 'x_original' in tmp: original = tmp['x_original'].shape else: original = False # add narr INFO to file global attributes: if 'attr' not in kargs: kargs['attr'] = {} try: s = '' for k in data: s += ' # ' + k.isoformat( ' ') + ' ' + data[k]['INFO_file'] + ' isbest ' + str( data[k]['INFO_isbest']) kargs['attr']['sources'] = s except: pass # common to interim---------------------- q = gennc.GenBlk(bulk, grd, **kargs) if create: q.create(model, original) for d in data: if model == 'roms': if not quiet: print(' converting units:'), conv_units(data[d], model, quiet) D = data2romsblk(data[d], g, **kargs) D['date'] = d if not quiet: print(' =>filling date=%s' % d.isoformat(' ')) q.fill(D, quiet=quiet)
def make_blk_cordex(cordexClim, grd, bulk, date0=False, date1=False, **kargs): ''' see make_blk_interim ps: to not include original data in file, use karg keepor=False ''' quiet = kargs.get('quiet', 0) create = kargs.get('create', 1) model = kargs.get('model', 'roms') # or roms-agrif proj = kargs.get('proj', 'auto') kargs['margin'] = 'no calc' # no need to extract a portion of the domain; # may be slow and is already done in cordex.py # besides, no need to do also for all variables as they share # the same domain data = load_blkdata_cordex( cordexClim, date0, date1, quiet, grd) # unique diff from make_blk_cordex/interim !! # grd is provided to extract just a portion of cordex domain, otherwise there is a memory error g = roms.Grid(grd) if proj == 'auto': kargs['proj'] = g.get_projection() # about original data, run data2romsblk once to test for x_original: tmp = data2romsblk(data[list(data.keys())[0]], g, **kargs) if 'x_original' in tmp: original = tmp['x_original'].shape else: original = False q = gennc.GenBlk(bulk, grd, **kargs) if create: q.create(model, original) for d in data: if model == 'roms': if not quiet: print(' converting units:'), conv_units(data[d], model, quiet) D = data2romsblk(data[d], g, **kargs) D['date'] = d if not quiet: print(' =>filling date=%s' % d.isoformat(' ')) q.fill(D, quiet=quiet)
def make_blk_era5(datapath, grd, bulk, date0=False, date1=False, **kargs): ''' see make_blk_interim ps: to not include original data in file, use karg keepor=False ''' quiet = kargs.get('quiet', 0) create = kargs.get('create', 1) model = kargs.get('model', 'roms') # or roms-agrif proj = kargs.get('proj', 'auto') data = load_blkdata_era5(datapath, date0, date1, quiet) g = roms.Grid(grd) if proj == 'auto': kargs['proj'] = g.get_projection() # about original data, run data2romsblk once to test for x_original: tmp = data2romsblk(list(data.values())[0], g, **kargs) if 'x_original' in tmp: original = tmp['x_original'].shape else: original = False # about wind speed and stress: wspeed = 'wspd' in list(data.values())[0] wstress = 'sustr' in list(data.values())[0] q = gennc.GenBlk(bulk, grd, **kargs) if create: q.create(model=model, original=original, wspeed=wspeed, wstress=wstress) for d in data: if model == 'roms': if not quiet: print(' converting units:'), conv_units(data[d], model, quiet) D = data2romsblk(data[d], g, **kargs) D['date'] = d if not quiet: print(' =>filling date=%s' % d.isoformat(' ')) q.fill(D, quiet=quiet)
def make_blk_cfsr(cfsrpath, grd, bulk, date0=False, date1=False, **kargs): ''' see make_blk_interim ps: to not include original data in file, use karg keepor=False ''' quiet = kargs.get('quiet', 0) create = kargs.get('create', 1) model = kargs.get('model', 'roms') # or roms-agrif proj = kargs.get('proj', 'auto') data = load_blkdata_cfsr(cfsrpath, date0, date1, quiet) # unique diff from make_blk_interim !! g = roms.Grid(grd) if proj == 'auto': kargs['proj'] = g.get_projection() # about original data, run data2romsblk once to test for x_original: tmp = data2romsblk(data[list(data.keys())[0]], g, **kargs) if 'x_original' in tmp: original = tmp['x_original'].shape else: original = False q = gennc.GenBlk(bulk, grd, **kargs) if create: q.create(model, original) for d in data: if model == 'roms': if not quiet: print(' converting units:'), conv_units(data[d], model, quiet) D = data2romsblk(data[d], g, **kargs) D['date'] = d if not quiet: print(' =>filling date=%s' % d.isoformat(' ')) q.fill(D, quiet=quiet)
def roms2roms(f0,grd,sparams,tind=0,**kargs): ''' tind: ind or datetime **kargs: grd0 sparams0 tunits0 quiet ''' grd0 = False sparams0 = False quiet = False tunits0 = False Z='auto' ij='i' for k in kargs.keys(): if k=='grd0': grd0 = kargs[k] elif k=='sparams0': sparams0 = kargs['sparams0'] elif k=='quiet': quiet = kargs['quiet'] elif k=='tunits0': tunits0 = kargs['tunits0'] elif k=='Z': Z = kargs['Z'] elif k=='ij': ij = kargs['ij'] r0=roms.His(f0,grd=grd0) g0=r0.grid g1=roms.Grid(grd) if sparams0 is False: sparams0=r0.s_params # load data: F0={} for i in ('temp','salt','u','v','ssh','time','misc'): F0[i]=f0 F0['xy']=g0.name if tind=='all': times=range(r0.TIME) else: times=[tind] xlim=g1.lon.min(),g1.lon.max() ylim=g1.lat.min(),g1.lat.max() inds={} outdata={} outdatab={} for tind in times: inds[sett.tdim]=tind data,msg=prognostic.load_data(F0,quiet=quiet,settings=sett,inds=inds,t_units=tunits0) # z3d: data['z3d']=r0.s_levels(tind) # u,v at rho: print ' u,v at rho ...' u=np.ma.zeros((data['NZ'],data['NY'],data['NX']),data['u'].dtype) v=np.ma.zeros((data['NZ'],data['NY'],data['NX']),data['v'].dtype) u[:,:,1:-1]=(data['u'][:,:,:-1]+data['u'][:,:,1:])/2. u[:,:,0]=data['u'][:,:,0] u[:,:,-1]=data['u'][:,:,-1] v[:,1:-1,:]=(data['v'][:,:-1,:]+data['v'][:,1:,:])/2. v[:,0,:]=data['v'][:,0,:] v[:,-1,:]=data['v'][:,-1,:] print 'rot 2d from original grid' for k in range(v.shape[0]): u[k],v[k]=calc.rot2d(u[k],v[k],-g0.angle) data['u']=u data['v']=v # simplify data: print ' simplify data...' i0,i1,j0,j1=calc.ij_limits(g0.lon,g0.lat,xlim,ylim,margin=3) for v in 'z3d','temp','salt','u','v': data[v]=data[v][:,j0:j1,i0:i1] for v in 'ssh','lon','lat': data[v]=data[v][j0:j1,i0:i1] data['NZ'],data['NY'],data['NX']=data['temp'].shape # interp depths: if Z is 'auto': h=-g0.h Z=np.concatenate((np.arange(data['ssh'].max(),-2,-.05),np.arange(-2,-5,.2),np.arange(-5,-20,-1),np.arange(-20,-100,-2), np.arange(-100,-500,-5),np.arange(-500,-1000,-20),np.arange(-1000,h.min()-100,-100))) Z=Z[::3] if Z[-1]>h.min(): Z[-1]=h.min() data['depth']=Z for v in 'temp','salt','u','v','ssh': print ' %-6s %6.3f %6.3f'%(v,data[v].min(), data[v].max()) # to z levels: Data=prognostic.data2z(data,quiet=quiet,ij=ij) for v in 'temp','salt','u','v','ssh': print ' %-6s %6.3f %6.3f'%(v,Data[v].min(), Data[v].max()) # clm: data,HA=prognostic.data2roms(Data,grd,sparams,quiet=quiet,horizAux=True,ij=ij) for v in 'temp','salt','u','v','zeta': print ' %-6s %6.3f %6.3f'%(v,data[v].min(), data[v].max()) # bry: datab=prognostic.data2romsbry(Data,grd,sparams,quiet=quiet,horizAux=HA) outdata[tind] = data outdatab[tind] = datab if len(times)==1: return outdata[tind],outdatab[tind] else: return outdata,outdatab
def update_wind(fname, data, new_wind_info, **kargs): ''' new_wind_info will be added to fname as global attribute (ex: 'new wind from database xxx') ''' quiet = False Margin = 4 # for griddata and for original data to keep grd = False keepOriginal = 2 for k in kargs.keys(): if k == 'quiet': quiet = kargs[k] elif k == 'margin': Margin = kargs[k] elif k == 'grid': grd = kargs[k] elif k.lower().startswith('keepor'): keepOriginal = kargs[k] if not grd: grd = netcdf.fatt(fname, 'grd_file') g = roms.Grid(grd) x0 = data['x'] y0 = data['y'] if x0.ndim == 1: x0, y0 = np.meshgrid(x0, y0) dates = data.keys()[:] dates.remove('x') dates.remove('y') dates = np.array(dates) # interp in time: time = netcdf.nctime(fname, 'time') cond = False tind = -1 fob = gennc.GenBlk(fname, grd) for t in time: newWind = {} tind += 1 I, = np.where(dates == t) if I.size: I = I[0] uv = data[dates[I]] if not quiet: print(t, dates[I]) else: i1, = np.where(dates > t) i0, = np.where(dates < t) if i0.size and i1.size: i1 = i1[0] i0 = i0[-1] d0 = t - dates[i0] d1 = dates[i1] - t d0 = d0.days + d0.seconds / 86400. d1 = d1.days + d1.seconds / 86400. uv = (data[dates[i0]] * d1 + data[dates[i1]] * d0) / (d0 + d1) if not quiet: print(t, dates[i0], dates[i1], d0, d1) elif not i1.size: uv = data[dates[-1]] if not quiet: print(t, dates[-1]) elif not i0.size: uv = data[dates[0]] if not quiet: print(t, dates[0]) # interp to grid: if cond is False: cond, inds = rt.grid_vicinity(grd, x0, y0, margin=Margin, rect=True, retinds=True) i1, i2, j1, j2 = inds if not quiet: print(' --> inter uv %s' % t.isoformat(' ')) u = calc.griddata(x0[cond], y0[cond], uv.real[cond], g.lon, g.lat, extrap=True) v = calc.griddata(x0[cond], y0[cond], uv.imag[cond], g.lon, g.lat, extrap=True) # rotate wind, calc stress: if not quiet: print(' --> rot U,V wind and U,V wind stress') wspd = np.sqrt(u**2 + v**2) sustr, svstr = air_sea.wind_stress(u, v) angle = g.use('angle') uwnd, vwnd = calc.rot2d(u, v, angle) sustr, svstr = calc.rot2d(sustr, svstr, angle) sustr = rt.rho2uvp(sustr, 'u') svstr = rt.rho2uvp(svstr, 'v') # update wind data: newWind['date'] = t newWind['uwnd'] = uwnd newWind['vwnd'] = vwnd newWind['sustr'] = sustr newWind['svstr'] = svstr newWind['wspd'] = wspd # original xy: if tind == 0: newWind['attr'] = {'new_wind_info': new_wind_info} if not quiet: print(' --> original xy') if keepOriginal == 1: newWind['x_wind'] = x0 newWind['y_wind'] = y0 elif keepOriginal == 2: newWind['x_wind'] = x0[j1:j2, i1:i2] newWind['y_wind'] = y0[j1:j2, i1:i2] # add to file: if not quiet: print(' --> adding to file') fob.update_wind(newWind, quiet=quiet) if not quiet: print('')
def data2romsblk(data, grd, **kargs): quiet = kargs.get('quiet', True) Margin = kargs.get('margin', 4) # for griddata and for original data to keep # if margin is 'no calc', no extraction of # a domain portion is done # about projection: # - if auto will use grid default projection # - if False, no projection is used # - any Basemap projection can be used proj = kargs.get('proj', 'auto') # about origina data to keep: # - if 2, will keep data only inside rt.grid_vicinity rectange # - if 1, all original data is kept keepOriginal = 2 for k in kargs: if k.lower().startswith('keepor'): keepOriginal = kargs[k] if cb.isstr(grd): g = roms.Grid(grd) else: g = grd if proj == 'auto': proj = g.get_projection() if proj: data_x, data_y = proj(data['x'], data['y']) grd_x, grd_y = proj(g.lon, g.lat) else: data_x, data_y = data['x'], data['y'] grd_x, grd_y = g.lon, g.lat cond = False out = {} for vname in data: if vname.startswith('INFO') or vname in 'xy': continue # vnames starting with INFO are an info key, without data if not quiet: print(' interp %s' % vname) if cond is False: if Margin == 'no calc': cond = np.ones_like(data['x'], 'bool') i1, i1 = 0, 0 j2, i2 = data['x'].shape else: cond, inds = rt.grid_vicinity(grd, data['x'], data['y'], margin=Margin, rect=True, retinds=True) i1, i2, j1, j2 = inds out[vname] = calc.griddata(data_x[cond], data_y[cond], data[vname][cond], grd_x, grd_y, extrap=True) if keepOriginal: # keep original data: if keepOriginal == 1: out[vname + '_original'] = data[vname] # keep all original data elif keepOriginal == 2: out[vname + '_original'] = data[vname][ j1:j2, i1:i2] # keep data only inside vicinity rectangle if 'x_original' not in out: if keepOriginal == 1: out['x_original'] = data['x'] out['y_original'] = data['y'] elif keepOriginal == 2: out['x_original'] = data['x'][j1:j2, i1:i2] out['y_original'] = data['y'][j1:j2, i1:i2] # about wind: if not quiet: print(' --> rot U,V wind and U,V wind stress') out['uwnd'], out['vwnd'] = calc.rot2d(out['uwnd'], out['vwnd'], g.angle) out['sustr'], out['svstr'] = calc.rot2d(out['sustr'], out['svstr'], g.angle) out['sustr'] = rt.rho2uvp(out['sustr'], 'u') out['svstr'] = rt.rho2uvp(out['svstr'], 'v') return out
def data2romsblk(data, grd, **kargs): quiet = True keepOriginal = 2 # if 2, will keep data only inside rt.grid_vicinity rectange # if 1, all original data is kept Margin = 4 # for griddata and for original data to keep for k in kargs.keys(): if k == 'quiet': quiet = kargs[k] elif k.lower().startswith('keepor'): keepOriginal = kargs[k] elif k == 'margin': Margin = kargs[k] g = roms.Grid(grd) cond = False out = {} for vname in data.keys(): if vname.startswith('INFO') or vname in 'xy': continue # vnames starting with INFO are an info key, without data if not quiet: print(' interp %s' % vname) if cond is False: cond, inds = rt.grid_vicinity(grd, data['x'], data['y'], margin=Margin, rect=True, retinds=True) i1, i2, j1, j2 = inds out[vname] = calc.griddata(data['x'][cond], data['y'][cond], data[vname][cond], g.lon, g.lat, extrap=True) if keepOriginal: # keep original data: if keepOriginal == 1: out[vname + '_original'] = data[vname] # keep all original data elif keepOriginal == 2: out[vname + '_original'] = data[vname][ j1:j2, i1:i2] # keep data only inside vicinity rectangle if not out.has_key('x_original'): if keepOriginal == 1: out['x_original'] = data['x'] out['y_original'] = data['y'] elif keepOriginal == 2: out['x_original'] = data['x'][j1:j2, i1:i2] out['y_original'] = data['y'][j1:j2, i1:i2] # about wind: if not quiet: print(' --> rot U,V wind and U,V wind stress') angle = g.use('angle') out['uwnd'], out['vwnd'] = calc.rot2d(out['uwnd'], out['vwnd'], angle) out['sustr'], out['svstr'] = calc.rot2d(out['sustr'], out['svstr'], angle) out['sustr'] = rt.rho2uvp(out['sustr'], 'u') out['svstr'] = rt.rho2uvp(out['svstr'], 'v') return out
def data2roms(data, grd, sparams, **kargs): ''' Interpolates data to roms 3D grid. The dict data must contain the prognostic variables temp, salt, u, v (3d) and ssh (zeta, 2d), as well as lon, lat (2d), depth (1d) and time/date info: date (data date), date0 (reference date) and time (difference between date and date0). The input data can be provided by load_data. Parameters ---------- data : dict with prognostic variables grd : ROMS netcdf grid file sparams : s-coordinates parameters, theta_s,theta_b, hc and NLevels **kargs: ij : axis for vertical interpolations (*i,j) ij_ind : list of i or j index for vertical interpolation, all by default (ij_ind=False) horizAux : if True, the original data horizontally interpolated is returned and can be used for next data2roms call with this same karg quiet : output messages flag (false by default) proj : projection - False, name or basemap proj - lcc by default if False, horizontal interpolations will use lonxlat instead of distances interp_opts: options for griddata rep_surf: repeat surface level (new upper level) ''' ij = kargs.get('ij', 'j') ij_ind = kargs.get('ij_ind', False) horizAux = kargs.get('horizAux', False) quiet = kargs.get('quiet', False) proj = kargs.get('proj', 'lcc') # lonxlat to distance before # horizontal interpolation interp_opts = kargs.get('interp_opts', {}) rep_surf = kargs.get('rep_surf', True) # create a surface upper level # before interpolation if not quiet: print 'using grid %s' % grd g = roms.Grid(grd) xr, yr, hr, mr = g.vars('r') xu, yu, hu, mu = g.vars('u') xv, yv, hv, mv = g.vars('v') ny, nx = hr.shape nz = sparams[3] if proj: print 'projecting coordinates...' if isinstance(proj, basestring): lonc = (xr.max() + xr.min()) / 2. latc = (yr.max() + yr.min()) / 2. from mpl_toolkits.basemap import Basemap proj = Basemap(projection=proj, width=1, height=1, resolution=None, lon_0=lonc, lat_0=latc, lat_1=latc) xr, yr = proj(xr, yr) xu, yu = proj(xu, yu) xv, yv = proj(xv, yv) dlon, dlat = proj(data['lon'], data['lat']) Rdz = 1 / 100. # distance to depth ratio (300km vs 3000m) distance = lambda x, y: np.append( 0., np.sqrt(np.diff(x)**2 + np.diff(y)**2).cumsum()) else: dlon, dlat = data['lon'], data['lat'] distance = calc.distance # needed for s_levels and for rep_surf! sshr = calc.griddata(dlon, dlat, data['ssh'], xr, yr, extrap=True, **interp_opts) # repeat surface: if rep_surf: # copy data cos dont want to change the original dataset: import copy data = copy.deepcopy(data) for vname in ['temp', 'salt', 'u', 'v', 'depth']: if data[vname].ndim == 1: # depth ! if np.ma.isMA(data[vname]): vstack = np.ma.hstack else: vstack = np.hstack else: if np.ma.isMA(data[vname]): vstack = np.ma.vstack else: vstack = np.vstack if data['depth'][0] > data['depth'][1]: # surf at ind 0 data[vname] = vstack((data[vname][0][np.newaxis], data[vname])) if vname == 'depth': data[vname][0] = sshr.max() else: data[vname] = vstack( (data[vname], data[vname][-1][np.newaxis])) if vname == 'depth': data[vname][-1] = sshr.max() data['NZ'] = data['NZ'] + 1 NX = data['NX'] NY = data['NY'] NZ = data['NZ'] if not quiet: print 'calc s levels...' Zr = g.s_levels(sparams, sshr, hr, 'r') Zu = g.s_levels(sparams, sshr, hr, 'u') Zv = g.s_levels(sparams, sshr, hr, 'v') # interp horiz: retHorizAux = horizAux is True if horizAux in (True, False): TEMP = np.ma.masked_all((NZ, ny, nx), data['temp'].dtype) SALT = np.ma.masked_all((NZ, ny, nx), data['salt'].dtype) U = np.ma.masked_all((NZ, ny, nx), data['u'].dtype) V = np.ma.masked_all((NZ, ny, nx), data['v'].dtype) if not quiet: print 'horizontal interpolation:' for i in range(NZ): if not quiet and i % 10 == 0: print ' lev %d of %d' % (i, NZ) #import pylab #pylab.figure() #pylab.pcolormesh(data['lon'],data['lat'],data['temp'][i,...]) try: TEMP[i, ...] = calc.griddata(dlon, dlat, data['temp'][i, ...], xr, yr, extrap=True, **interp_opts) except: pass try: SALT[i, ...] = calc.griddata(dlon, dlat, data['salt'][i, ...], xr, yr, extrap=True, **interp_opts) except: pass try: U[i, ...] = calc.griddata(dlon, dlat, data['u'][i, ...], xr, yr, extrap=True, **interp_opts) except: pass try: V[i, ...] = calc.griddata(dlon, dlat, data['v'][i, ...], xr, yr, extrap=True, **interp_opts) except: pass # rotate U,V: if not quiet: print 'rotating U,V to grid angle' angle = g.use('angle') # rad U, V = calc.rot2d(U, V, angle) U = rt.rho2uvp3d(U, 'u') V = rt.rho2uvp3d(V, 'v') horizAux = {} horizAux['TEMP'] = TEMP horizAux['SALT'] = SALT horizAux['U'] = U horizAux['V'] = V else: TEMP = horizAux['TEMP'] SALT = horizAux['SALT'] U = horizAux['U'] V = horizAux['V'] # interp vert: nxu = nx - 1 nyv = ny - 1 #> ----------------------------------------------------------------- useInd = not ij_ind is False if ij_ind is False: if ij == 'j': ij_ind = range(ny) elif ij == 'i': ij_ind = range(nx) else: try: iter(ij_ind) except: ij_ind = [ij_ind] if ij == 'j': ny = nyv = len(ij_ind) elif ij == 'i': nx = nxu = len(ij_ind) # -----------------------------------------------------------------< Temp = np.zeros((nz, ny, nx), data['temp'].dtype) Salt = np.zeros((nz, ny, nx), data['salt'].dtype) Uvel = np.zeros((nz, ny, nxu), data['u'].dtype) Vvel = np.zeros((nz, nyv, nx), data['v'].dtype) jslice = lambda x, ind: x[:, ind, :] islice = lambda x, ind: x[:, :, ind] ZZr = np.tile(data['depth'], (nx, ny, 1)).T ZZu = np.tile(data['depth'], (nxu, ny, 1)).T ZZv = np.tile(data['depth'], (nx, nyv, 1)).T if not useInd is False: #>------------------------------------------ if ij == 'j': slice = jslice sshr = sshr[ij_ind, :] hr = hr[ij_ind, :] elif ij == 'i': slice = islice sshr = sshr[:, ij_ind] hr = hr[:, ij_ind] Zr, Zu, Zv, TEMP, SALT, U, V = [ slice(k, ij_ind) for k in [Zr, Zu, Zv, TEMP, SALT, U, V] ] # -----------------------------------------------------------------< if useInd: # then store distances for a possible bry file dtype = Temp.dtype distr = np.zeros((nz, ny, nx), dtype) distu = np.zeros((nz, ny, nxu), dtype) distv = np.zeros((nz, nyv, nx), dtype) if not quiet: print 'vertical interpolation:' if ij == 'j': for j in range(ny): if not quiet and (ny < 10 or (ny >= 10 and j % 10 == 0)): print ' j=%3d of %3d' % (j, ny) ind = ij_ind[j] dr = np.tile(distance(xr[ind, :], yr[ind, :]), (nz, 1)) du = np.tile(distance(xu[ind, :], yu[ind, :]), (nz, 1)) Dr = np.tile(distance(xr[ind, :], yr[ind, :]), (NZ, 1)) Du = np.tile(distance(xu[ind, :], yu[ind, :]), (NZ, 1)) if useInd: distr[:, j, :] = dr distu[:, j, :] = du Temp[:, j, :] = calc.griddata(Rdz * Dr, ZZr[:, j, :], TEMP[:, j, :], Rdz * dr, Zr[:, j, :], extrap=True, **interp_opts) Salt[:, j, :] = calc.griddata(Rdz * Dr, ZZr[:, j, :], SALT[:, j, :], Rdz * dr, Zr[:, j, :], extrap=True, **interp_opts) if 0 and j % 10 == 0: print Dr.shape, ZZr[:, j, :].shape import pylab as pl pl.figure(1) pl.clf() pl.pcolormesh(Dr, ZZr[:, j, :], SALT[:, j, :]) pl.colorbar() clim = pl.gci().get_clim() pl.figure(2) pl.clf() pl.pcolormesh(dr, Zr[:, j, :], Salt[:, j, :]) pl.clim(clim) pl.colorbar() raw_input() Uvel[:, j, :] = calc.griddata(Rdz * Du, ZZu[:, j, :], U[:, j, :], Rdz * du, Zu[:, j, :], extrap=True, **interp_opts) if j < Vvel.shape[1]: dv = np.tile(distance(xv[ind, :], yv[ind, :]), (nz, 1)) Dv = np.tile(distance(xv[ind, :], yv[ind, :]), (NZ, 1)) Vvel[:, j, :] = calc.griddata(Rdz * Dv, ZZv[:, j, :], V[:, j, :], Rdz * dv, Zv[:, j, :], extrap=True, **interp_opts) if useInd: distv[:, j, :] = dv if np.any(np.isnan(Temp[:, j, :])): print 'found nan in temp', j if np.any(np.isnan(Salt[:, j, :])): print 'found nan in salt', j if np.any(np.isnan(Uvel[:, j, :])): print 'found nan in u', j if j < Vvel.shape[1] and np.any(np.isnan(Vvel[:, j, :])): print 'found nan in v', j elif ij == 'i': for i in range(nx): if not quiet and (nx < 10 or (nx >= 10 and i % 10 == 0)): print ' i=%3d of %3d' % (i, nx) ind = ij_ind[i] dr = np.tile(distance(xr[:, ind], yr[:, ind]), (nz, 1)) dv = np.tile(distance(xv[:, ind], yv[:, ind]), (nz, 1)) Dr = np.tile(distance(xr[:, ind], yr[:, ind]), (NZ, 1)) Dv = np.tile(distance(xv[:, ind], yv[:, ind]), (NZ, 1)) if useInd: distr[:, :, i] = dr distv[:, :, i] = dv Temp[:, :, i] = calc.griddata(Rdz * Dr, ZZr[:, :, i], TEMP[:, :, i], Rdz * dr, Zr[:, :, i], extrap=True, **interp_opts) Salt[:, :, i] = calc.griddata(Rdz * Dr, ZZr[:, :, i], SALT[:, :, i], Rdz * dr, Zr[:, :, i], extrap=True, **interp_opts) Vvel[:, :, i] = calc.griddata(Rdz * Dv, ZZv[:, :, i], V[:, :, i], Rdz * dv, Zv[:, :, i], extrap=True, **interp_opts) if i < Uvel.shape[2]: du = np.tile(distance(xu[:, ind], yu[:, ind]), (nz, 1)) Du = np.tile(distance(xu[:, ind], yu[:, ind]), (NZ, 1)) Uvel[:, :, i] = calc.griddata(Rdz * Du, ZZu[:, :, i], U[:, :, i], Rdz * du, Zu[:, :, i], extrap=True, **interp_opts) if useInd: distu[:, :, i] = du # uv bar: if not quiet: print 'calc uvbar' if useInd is False: ubar, vbar = rt.uvbar(Uvel, Vvel, sshr, hr, sparams) else: #>------------------------------------------------------------ sshu = calc.griddata(dlon, dlat, data['ssh'], xu, yu, extrap=True, **interp_opts) sshv = calc.griddata(dlon, dlat, data['ssh'], xv, yv, extrap=True, **interp_opts) if ij == 'j': sshu = sshu[ij_ind, :] sshv = sshv[ij_ind, :] hu = hu[ij_ind, :] hv = hv[ij_ind, :] elif ij == 'i': sshu = sshu[:, ij_ind] sshv = sshv[:, ij_ind] hu = hu[:, ij_ind] hv = hv[:, ij_ind] ubar = rt.barotropic(Uvel, sshu, hu, sparams) vbar = rt.barotropic(Vvel, sshv, hv, sparams) # -----------------------------------------------------------------< Vars = cb.odict() Vars['temp'] = Temp Vars['salt'] = Salt Vars['u'] = Uvel Vars['v'] = Vvel Vars['zeta'] = sshr Vars['ubar'] = ubar Vars['vbar'] = vbar Vars['date'] = data['date'] if not useInd is False: #>------------------------------------------ Vars['depth'] = Zr Vars['depthu'] = Zu Vars['depthv'] = Zv Vars['dist'] = distr Vars['distu'] = distu Vars['distv'] = distv # -----------------------------------------------------------------< if retHorizAux: return Vars, horizAux else: return Vars
def data2roms(data, grd, sparams, **kargs): ''' Interpolates data to roms 3D grid. The dict data must contain the prognostic variables temp, salt, u, v (3d) and ssh (zeta, 2d), as well as lon, lat (2d), depth (1d) and time/date info: date (data date), date0 (reference date) and time (difference between date and date0). The input data can be provided by load_data. Parameters ---------- data : dict with prognostic variables grd : ROMS netcdf grid file sparams : s-coordinates parameters, theta_s,theta_b, hc and NLevels **kargs: ij : axis for vertical interpolations (*i,j) ij_ind : list of i or j index for vertical interpolation, all by default (ij_ind=False) horizAux : if True, the original data horizontally interpolated is returned and can be used for next data2roms call with this same karg quiet : output messages flag (false by default) ''' ij = 'j' ij_ind = False horizAux = False quiet = False if 'ij' in kargs.keys(): ij = kargs['ij'] if 'ij_ind' in kargs.keys(): ij_ind = kargs['ij_ind'] if 'horizAux' in kargs.keys(): horizAux = kargs['horizAux'] if 'quiet' in kargs.keys(): quiet = kargs['quiet'] if not quiet: print 'using grid %s' % grd g = roms.Grid(grd) xr, yr, hr, mr = g.vars('r') xu, yu, hu, mu = g.vars('u') xv, yv, hv, mv = g.vars('v') ny, nx = hr.shape nz = sparams[3] NX = data['NX'] NY = data['NY'] NZ = data['NZ'] if not quiet: print 'calc s levels...' sshr = calc.griddata(data['lon'], data['lat'], data['ssh'], xr, yr, extrap=True) Zr = g.s_levels(sparams, sshr, hr, 'r') Zu = g.s_levels(sparams, sshr, hr, 'u') Zv = g.s_levels(sparams, sshr, hr, 'v') # interp horiz: retHorizAux = horizAux is True if horizAux in (True, False): TEMP = np.ma.masked_all((NZ, ny, nx), data['temp'].dtype) SALT = np.ma.masked_all((NZ, ny, nx), data['salt'].dtype) U = np.ma.masked_all((NZ, ny, nx), data['u'].dtype) V = np.ma.masked_all((NZ, ny, nx), data['v'].dtype) if not quiet: print 'horizontal interpolation:' for i in range(NZ): if not quiet and i % 10 == 0: print ' lev %d of %d' % (i, NZ) #import pylab #pylab.figure() #pylab.pcolormesh(data['lon'],data['lat'],data['temp'][i,...]) try: TEMP[i, ...] = calc.griddata(data['lon'], data['lat'], data['temp'][i, ...], xr, yr, extrap=True) except: pass try: SALT[i, ...] = calc.griddata(data['lon'], data['lat'], data['salt'][i, ...], xr, yr, extrap=True) except: pass try: U[i, ...] = calc.griddata(data['lon'], data['lat'], data['u'][i, ...], xr, yr, extrap=True) except: pass try: V[i, ...] = calc.griddata(data['lon'], data['lat'], data['v'][i, ...], xr, yr, extrap=True) except: pass # rotate U,V: if not quiet: print 'rotating U,V to grid angle' angle = g.use('angle') # rad U, V = calc.rot2d(U, V, angle) U = rt.rho2uvp3d(U, 'u') V = rt.rho2uvp3d(V, 'v') horizAux = {} horizAux['TEMP'] = TEMP horizAux['SALT'] = SALT horizAux['U'] = U horizAux['V'] = V else: TEMP = horizAux['TEMP'] SALT = horizAux['SALT'] U = horizAux['U'] V = horizAux['V'] # interp vert: nxu = nx - 1 nyv = ny - 1 #> ----------------------------------------------------------------- useInd = not ij_ind is False if ij_ind is False: if ij == 'j': ij_ind = range(ny) elif ij == 'i': ij_ind = range(nx) else: try: iter(ij_ind) except: ij_ind = [ij_ind] if ij == 'j': ny = nyv = len(ij_ind) elif ij == 'i': nx = nxu = len(ij_ind) # -----------------------------------------------------------------< Temp = np.zeros((nz, ny, nx), data['temp'].dtype) Salt = np.zeros((nz, ny, nx), data['salt'].dtype) Uvel = np.zeros((nz, ny, nxu), data['u'].dtype) Vvel = np.zeros((nz, nyv, nx), data['v'].dtype) jslice = lambda x, ind: x[:, ind, :] islice = lambda x, ind: x[:, :, ind] ZZr = np.tile(data['depth'], (nx, ny, 1)).T ZZu = np.tile(data['depth'], (nxu, ny, 1)).T ZZv = np.tile(data['depth'], (nx, nyv, 1)).T if not useInd is False: #>------------------------------------------ if ij == 'j': slice = jslice sshr = sshr[ij_ind, :] hr = hr[ij_ind, :] elif ij == 'i': slice = islice sshr = sshr[:, ij_ind] hr = hr[:, ij_ind] Zr, Zu, Zv, TEMP, SALT, U, V = [ slice(k, ij_ind) for k in [Zr, Zu, Zv, TEMP, SALT, U, V] ] # -----------------------------------------------------------------< if useInd: # then store distances for a possible bry file dtype = Temp.dtype distr = np.zeros((nz, ny, nx), dtype) distu = np.zeros((nz, ny, nxu), dtype) distv = np.zeros((nz, nyv, nx), dtype) if not quiet: print 'vertical interpolation:' if ij == 'j': for j in range(ny): if not quiet and (ny < 10 or (ny >= 10 and j % 10 == 0)): print ' j=%3d of %3d' % (j, ny) ind = ij_ind[j] dr = np.tile(calc.distance(xr[ind, :], yr[ind, :]), (nz, 1)) du = np.tile(calc.distance(xu[ind, :], yu[ind, :]), (nz, 1)) Dr = np.tile(calc.distance(xr[ind, :], yr[ind, :]), (NZ, 1)) Du = np.tile(calc.distance(xu[ind, :], yu[ind, :]), (NZ, 1)) if useInd: distr[:, j, :] = dr distu[:, j, :] = du Temp[:, j, :] = calc.griddata(Dr, ZZr[:, j, :], TEMP[:, j, :], dr, Zr[:, j, :], extrap=True) Salt[:, j, :] = calc.griddata(Dr, ZZr[:, j, :], SALT[:, j, :], dr, Zr[:, j, :], extrap=True) if 0 and j % 10 == 0: print Dr.shape, ZZr[:, j, :].shape import pylab as pl pl.figure(1) pl.clf() pl.pcolormesh(Dr, ZZr[:, j, :], SALT[:, j, :]) pl.colorbar() clim = pl.gci().get_clim() pl.figure(2) pl.clf() pl.pcolormesh(dr, Zr[:, j, :], Salt[:, j, :]) pl.clim(clim) pl.colorbar() raw_input() Uvel[:, j, :] = calc.griddata(Du, ZZu[:, j, :], U[:, j, :], du, Zu[:, j, :], extrap=True) if j < Vvel.shape[1]: dv = np.tile(calc.distance(xv[ind, :], yv[ind, :]), (nz, 1)) Dv = np.tile(calc.distance(xv[ind, :], yv[ind, :]), (NZ, 1)) Vvel[:, j, :] = calc.griddata(Dv, ZZv[:, j, :], V[:, j, :], dv, Zv[:, j, :], extrap=True) if useInd: distv[:, j, :] = dv if np.any(np.isnan(Temp[:, j, :])): print 'found nan in temp', j if np.any(np.isnan(Salt[:, j, :])): print 'found nan in salt', j if np.any(np.isnan(Uvel[:, j, :])): print 'found nan in u', j if j < Vvel.shape[1] and np.any(np.isnan(Vvel[:, j, :])): print 'found nan in v', j elif ij == 'i': for i in range(nx): if not quiet and (nx < 10 or (nx >= 10 and i % 10 == 0)): print ' i=%3d of %3d' % (i, nx) ind = ij_ind[i] dr = np.tile(calc.distance(xr[:, ind], yr[:, ind]), (nz, 1)) dv = np.tile(calc.distance(xv[:, ind], yv[:, ind]), (nz, 1)) Dr = np.tile(calc.distance(xr[:, ind], yr[:, ind]), (NZ, 1)) Dv = np.tile(calc.distance(xv[:, ind], yv[:, ind]), (NZ, 1)) if useInd: distr[:, :, i] = dr distv[:, :, i] = dv Temp[:, :, i] = calc.griddata(Dr, ZZr[:, :, i], TEMP[:, :, i], dr, Zr[:, :, i], extrap=True) Salt[:, :, i] = calc.griddata(Dr, ZZr[:, :, i], SALT[:, :, i], dr, Zr[:, :, i], extrap=True) Vvel[:, :, i] = calc.griddata(Dv, ZZv[:, :, i], V[:, :, i], dv, Zv[:, :, i], extrap=True) if i < Uvel.shape[2]: du = np.tile(calc.distance(xu[:, ind], yu[:, ind]), (nz, 1)) Du = np.tile(calc.distance(xu[:, ind], yu[:, ind]), (NZ, 1)) Uvel[:, :, i] = calc.griddata(Du, ZZu[:, :, i], U[:, :, i], du, Zu[:, :, i], extrap=True) if useInd: distu[:, :, i] = du # uv bar: if not quiet: print 'calc uvbar' if useInd is False: ubar, vbar = rt.uvbar(Uvel, Vvel, sshr, hr, sparams) else: #>------------------------------------------------------------ sshu = calc.griddata(data['lon'], data['lat'], data['ssh'], xu, yu, extrap=True) sshv = calc.griddata(data['lon'], data['lat'], data['ssh'], xv, yv, extrap=True) if ij == 'j': sshu = sshu[ij_ind, :] sshv = sshv[ij_ind, :] hu = hu[ij_ind, :] hv = hv[ij_ind, :] elif ij == 'i': sshu = sshu[:, ij_ind] sshv = sshv[:, ij_ind] hu = hu[:, ij_ind] hv = hv[:, ij_ind] ubar = rt.barotropic(Uvel, sshu, hu, sparams) vbar = rt.barotropic(Vvel, sshv, hv, sparams) # -----------------------------------------------------------------< Vars = cb.odict() Vars['temp'] = Temp Vars['salt'] = Salt Vars['u'] = Uvel Vars['v'] = Vvel Vars['zeta'] = sshr Vars['ubar'] = ubar Vars['vbar'] = vbar Vars['date'] = data['date'] if not useInd is False: #>------------------------------------------ Vars['depth'] = Zr Vars['depthu'] = Zu Vars['depthv'] = Zv Vars['dist'] = distr Vars['distu'] = distu Vars['distv'] = distv # -----------------------------------------------------------------< if retHorizAux: return Vars, horizAux else: return Vars