tpxo_extract_HC.py

'''
This is to extract the tidal harmonic constants out of a tidal model
for a given locations
[amp,Gph] = tpxo_extract_HC(Model,lat,lon,type,Cid)

@author: Mr. Srikanth Nagella
'''

# pylint: disable=E1103
# pylint: disable=no-name-in-module
from netCDF4 import Dataset
from scipy import interpolate
import numpy as np

class TpxoExtract(object):
    """ This is TPXO model extract_hc.c implementation in python"""
    def __init__(self, settings, lat, lon, grid_type):
        """initialises the Extract of tide information from the netcdf
           Tidal files"""
	# Set tide model
        tide_model = 'TPXO'

        if tide_model == 'TPXO':  # Define stuff to generalise Tide model
           hRe_name = 'hRe'
           hIm_name = 'hIm'
           lon_z_name = 'lon_z'
           lat_z_name = 'lat_z'	
           URe_name = 'URe'
           UIm_name = 'UIm'
           lon_u_name = 'lon_u'
           lat_u_name = 'lat_u'
           VRe_name = 'VRe'
           VIm_name = 'VIm'
           lon_v_name = 'lon_v'
           lat_v_name = 'lat_v'
           mz_name = 'mz'
           mu_name = 'mu'
           mv_name = 'mv'
           self.grid = Dataset(settings['tide_grid'])#../data/tide/grid_tpxo7.2.nc')
           #read the height_dataset file
           self.height_dataset = Dataset(settings['tide_h'])#../data/tide/h_tpxo7.2.nc')
           #read the velocity_dataset file
           self.velocity_dataset = Dataset(settings['tide_u'])#../data/tide/u_tpxo7.2.nc')

           height_z = self.grid.variables['hz']
           mask_z = self.grid.variables['mz']
           lon_z = self.height_dataset.variables[lon_z_name][:, 0]
           lat_z = self.height_dataset.variables[lat_z_name][0, :]
           lon_resolution = lon_z[1] - lon_z[0]
           data_in_km = 0 # added to maintain the reference to matlab tmd code
           # Pull out the constituents that are avaibable
           self.cons = []
           for ncon in range(self.height_dataset.variables['con'].shape[0]):
              self.cons.append(self.height_dataset.variables['con'][ncon, :].tostring().strip())
        elif tide_model == 'FES':
           constituents = ['2N2','EPS2','J1','K1','K2','L2','LA2','M2','M3','M4','M6','M8','MF','MKS2','MM','MN4','MS4','MSF','MSQM','MTM','MU2','N2','N4','NU2','O1','P1','Q1','R2','S1','S2','S4','SA','SSA','T2']
           print('did not actually code stuff for FES in this routine. Though that would be ideal. Instead put it in fes_extract_HC.py')
        else:
           print('Don''t know that tide model')

        # Wrap coordinates in longitude if the domain is global
        glob = 0
        if lon_z[-1]-lon_z[0] == 360-lon_resolution:
            glob = 1
        if glob == 1:
            lon_z = np.concatenate(([lon_z[0]-lon_resolution, ], lon_z,
                                    [lon_z[-1]+lon_resolution, ]))
            height_z = np.concatenate(([height_z[-1, :], ], height_z, [height_z[0, :],]), axis=0)
            mask_z = np.concatenate(([mask_z[-1, :], ], mask_z, [mask_z[0, :], ]), axis=0)

        #adjust lon convention
        xmin = np.min(lon)

        if data_in_km == 0:
            if xmin < lon_z[0]:
                lon[lon < 0] = lon[lon < 0] + 360
            if xmin > lon_z[-1]:
                lon[lon > 180] = lon[lon > 180]-360

        #height_z[height_z==0] = np.NaN
#        f=interpolate.RectBivariateSpline(lon_z,lat_z,height_z,kx=1,ky=1)
#        depth = np.zeros(lon.size)
#        for idx in range(lon.size):
#            depth[idx] = f(lon[idx],lat[idx])
#        print depth[369:371]

#        H2 = np.ravel(height_z)
#        H2[H2==0] = np.NaN
#        points= np.concatenate((np.ravel(self.height_dataset.variables['lon_z']),
#                                np.ravel(self.height_dataset.variables['lat_z'])))
#        points= np.reshape(points,(points.shape[0]/2,2),order='F')
#        print points.shape
#        print np.ravel(height_z).shape
#        depth = interpolate.griddata(points,H2,(lon,lat))
#        print depth
#        print depth.shape

        height_z[height_z == 0] = np.NaN
        lonlat = np.concatenate((lon, lat))
        lonlat = np.reshape(lonlat, (lon.size, 2), order='F')

        depth = interpolate.interpn((lon_z, lat_z), height_z, lonlat)
#        f=interpolate.RectBivariateSpline(lon_z,lat_z,mask_z,kx=1,ky=1)
#        depth_mask = np.zeros(lon.size)
#        for idx in range(lon.size):
#            depth_mask[idx] = f(lon[idx],lat[idx])
        depth_mask = interpolate.interpn((lon_z, lat_z), mask_z, lonlat)
        index = np.where((np.isnan(depth)) & (depth_mask > 0))

        if index[0].size != 0:
            depth[index] = bilinear_interpolation(lon_z, lat_z, height_z, lon[index], lat[index])

        if grid_type == 'z' or grid_type == 't':
            self.amp, self.gph = self.interpolate_constituents(self.height_dataset,
                                                               hRe_name, hIm_name, lon_z_name, lat_z_name,
							       lon, lat, maskname=mz_name)
        elif grid_type == 'u':
            self.amp, self.gph = self.interpolate_constituents(self.velocity_dataset,
                                                               URe_name, UIm_name, lon_u_name, lat_u_name,
                                                               lon, lat, depth, maskname=mu_name)
        elif grid_type == 'v':
            self.amp, self.gph = self.interpolate_constituents(self.velocity_dataset,
                                                               VRe_name, VIm_name, lon_v_name, lat_v_name,
                                                               lon, lat, depth, maskname=mv_name)
        else:
            print('Unknown grid_type')
            return

    def interpolate_constituents(self, nc_dataset, real_var_name, img_var_name, lon_var_name,
                                 lat_var_name, lon, lat, height_data=None, maskname=None):
        """ Interpolates the tidal constituents along the given lat lon coordinates """
        amp = np.zeros((nc_dataset.variables['con'].shape[0], lon.shape[0],))
        gph = np.zeros((nc_dataset.variables['con'].shape[0], lon.shape[0],))
        data = np.array(np.ravel(nc_dataset.variables[real_var_name]), dtype=complex)
        data.imag = np.array(np.ravel(nc_dataset.variables[img_var_name]))

        data = data.reshape(nc_dataset.variables[real_var_name].shape)
        #data[data==0] = np.NaN

        #Lat Lon values
        x_values = nc_dataset.variables[lon_var_name][:, 0]
        y_values = nc_dataset.variables[lat_var_name][0, :]
        x_resolution = x_values[1] - x_values[0]
        glob = 0
        if x_values[-1]-x_values[0] == 360-x_resolution:
            glob = 1

        if glob == 1:
            x_values = np.concatenate(([x_values[0]-x_resolution,], x_values,
                                       [x_values[-1]+x_resolution, ]))

        #adjust lon convention
        xmin = np.min(lon)
        if xmin < x_values[0]:
            lon[lon < 0] = lon[lon < 0] + 360
        if xmin > x_values[-1]:
            lon[lon > 180] = lon[lon > 180]-360

        lonlat = np.concatenate((lon, lat))
        lonlat = np.reshape(lonlat, (lon.size, 2), order='F')

        mask = self.grid.variables[maskname]
        mask = np.concatenate(([mask[-1, :], ], mask, [mask[0, :], ]), axis=0)
        #interpolate the mask values
        maskedpoints = interpolate.interpn((x_values, y_values), mask, lonlat)

        data_temp = np.zeros((data.shape[0], lon.shape[0], 2, ))
        for cons_index in range(data.shape[0]):
            #interpolate real values
            data_temp[cons_index, :, 0] = interpolate_data(x_values, y_values,
                                                                data[cons_index, :, :].real,
                                                                maskedpoints, lonlat)
            #interpolate imag values
            data_temp[cons_index, :, 1] = interpolate_data(x_values, y_values,
                                                                data[cons_index, :, :].imag,
                                                                maskedpoints, lonlat)

            #for velocity_dataset values
            if height_data is not None:
                data_temp[cons_index, :, 0] = data_temp[cons_index, :, 0]/height_data*100
                data_temp[cons_index, :, 1] = data_temp[cons_index, :, 1]/height_data*100

            zcomplex = np.array(data_temp[cons_index, :, 0], dtype=complex)
            zcomplex.imag = data_temp[cons_index, :, 1]

            amp[cons_index, :] = np.absolute(zcomplex)
            gph[cons_index, :] = np.arctan2(-1*zcomplex.imag, zcomplex.real)
        gph = gph*180.0/np.pi
        gph[gph < 0] = gph[gph < 0]+360.0
        return amp, gph

def interpolate_data(lon, lat, data, mask, lonlat):
    """ Interpolate data data on regular grid for given lonlat coordinates """
    result = np.zeros((lonlat.shape[0], ))
    data[data == 0] = np.NaN
    data = np.concatenate(([data[-1, :], ], data, [data[0, :], ]), axis=0)
    result[:] = interpolate.interpn((lon, lat), data, lonlat)
    index = np.where((np.isnan(result)) & (mask > 0))
    if index[0].size != 0:
        result[index] = bilinear_interpolation(lon, lat, data, np.ravel(lonlat[index, 0]),
                                               np.ravel(lonlat[index, 1]))
    return result

def bilinear_interpolation(lon, lat, data, lon_new, lat_new):
    """ Does a bilinear interpolation of grid where the data values are NaN's"""
    glob = 0
    lon_resolution = lon[1] - lon[0]
    if lon[-1] - lon[1] == 360 - lon_resolution:
        glob = 1
    inan = np.where(np.isnan(data))
    data[inan] = 0
    mask = np.zeros(data.shape)
    mask[data != 0] = 1
#    n = lon.size
#    m = lat.size
    if lon.size != data.shape[0] or lat.size != data.shape[1]:
        print('Check Dimensions')
        return np.NaN
    if glob == 1:
        lon = np.concatenate(([lon[0] - 2 * lon_resolution, lon[0] - lon_resolution, ],
                              lon, [lon[-1] + lon_resolution, lon[-1] + 2 * lon_resolution]))
        data = np.concatenate((data[-2, :], data[-1, :], data, data[0, :], data[1, :]), axis=0)
        mask = np.concatenate((mask[-2, :], mask[-1, :], mask, mask[0, :], mask[1, :]), axis=0)
    lon_new_copy = lon_new

    nonmask_index = np.where((lon_new_copy < lon[0]) & (lon_new_copy > lon[-1]))
    if lon[-1] > 180:
        lon_new_copy[nonmask_index] = lon_new_copy[nonmask_index] + 360
    if lon[-1] < 0:
        lon_new_copy[nonmask_index] = lon_new_copy[nonmask_index] - 360
    lon_new_copy[lon_new_copy > 360] = lon_new_copy[lon_new_copy > 360] - 360
    lon_new_copy[lon_new_copy < -180] = lon_new_copy[lon_new_copy < -180] + 360

    weight_factor_0 = 1 / (4 + 2 * np.sqrt(2))
    weight_factor_1 = weight_factor_0 / np.sqrt(2)
    height_temp = weight_factor_1 * data[0:-2, 0:-2] + weight_factor_0 * data[0:-2, 1:-1] + \
                  weight_factor_1 * data[0:-2, 2:] + weight_factor_1 * data[2:, 0:-2] + \
                  weight_factor_0 * data[2:, 1:-1] + weight_factor_1 * data[2:, 2:] + \
                  weight_factor_0 * data[1:-1, 0:-2] + weight_factor_0 * data[1:-1, 2:]
    mask_temp = weight_factor_1 * mask[0:-2, 0:-2] + weight_factor_0 * mask[0:-2, 1:-1] + \
                weight_factor_1 * mask[0:-2, 2:] + weight_factor_1 * mask[2:, 0:-2] + \
                weight_factor_0 * mask[2:, 1:-1] + weight_factor_1 * mask[2:, 2:] + \
                weight_factor_0 * mask[1:-1, 0:-2] + weight_factor_0 * mask[1:-1, 2:]
    mask_temp[mask_temp == 0] = 1
    data_copy = data.copy()
    data_copy[1:-1, 1:-1] = np.divide(height_temp, mask_temp)
    nonmask_index = np.where(mask == 1)
    data_copy[nonmask_index] = data[nonmask_index]
    data_copy[data_copy == 0] = np.NaN
    lonlat = np.concatenate((lon_new_copy, lat_new))
    lonlat = np.reshape(lonlat, (lon_new_copy.size, 2), order='F')
    result = interpolate.interpn((lon, lat), data_copy, lonlat)

    return result


#lat=[42.8920,42.9549,43.0178]
#lon=[339.4313,339.4324,339.4335]
#lat_u=[42.8916,42.9545,43.0174]
#lon_u=[339.4735,339.4746,339.4757]
#lat = np.array(lat_u)
#lon = np.array(lon_u)
#lon = TPXO_Extract(lat,lon,'velocity_dataset')