'''
'''
# 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)