'''
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 HcExtract(object):
""" This is FES model extract_hc.c implementation in python adapted from tpxo_extract_HC.py"""
def __init__(self, settings, lat, lon, grid_type):
"""initialises the Extract of tide information from the netcdf
Tidal files"""
# Set tide model
tide_model = 'fes'
if tide_model == 'fes': # 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 = 'mask_z'
mu_name = 'mask_u'
mv_name = 'mask_v'
# create list of HC using namelist file as reference
constituents = list(settings['clname'].values())
# clean strings in list and change to upper case if not already
for i in range(len(constituents)):
constituents[i] = constituents[i].strip("',/\n")
constituents[i] = constituents[i].upper()
self.cons = constituents
self.mask_dataset = {}
# extract lon and lat z data
lon_z = np.array(Dataset(settings['tide_fes']+constituents[0]+'_Z.nc').variables['lon'])
lat_z = np.array(Dataset(settings['tide_fes']+constituents[0]+'_Z.nc').variables['lat'])
lon_resolution = lon_z[1] - lon_z[0]
data_in_km = 0 # added to maintain the reference to matlab tmd code
if grid_type == 'z' or grid_type == 't':
# extract example amplitude grid for Z, U and V and change NaNs to 0 (for land) and other values to 1 (for water)
mask = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['amplitude'][:])))
mask[mask != 18446744073709551616.00000] = 1
mask[mask == 18446744073709551616.00000] = 0
self.mask_dataset[mz_name] = mask
#read and convert the height_dataset file to complex and store in dicts
hRe = []
hIm = []
lat_z = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['lat'][:])
lon_z = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_Z.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+str(constituents[ncon])+'_Z.nc').variables['amplitude'][:])))
# set fill values to zero
amp[amp == 18446744073709551616.00000] = 0
# convert amp to m from cm
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_Z.nc').variables['phase'][:])))
# set fill values to 0
phase[phase == 18446744073709551616.00000] = 0
# convert to real and imaginary conjugates and also convert to radians.
hRe.append(amp*np.cos(phase*(np.pi/180)))
hIm.append(-amp*np.sin(phase*(np.pi/180)))
hRe = np.stack(hRe)
hIm = np.stack(hIm)
self.height_dataset = [lon_z,lat_z,hRe,hIm]
elif grid_type == 'u':
mask = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['Ua'][:])))
mask[mask != 18446744073709551616.00000] = 1
mask[mask == 18446744073709551616.00000] = 0
self.mask_dataset[mu_name] = mask
#read and convert the velocity_dataset files to complex
URe = []
UIm = []
lat_u = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['lat'][:])
lon_u = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_U.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ua'][:])))
# set fill values to zero
amp[amp == 18446744073709551616.00000] = 0
# convert amp units to m/s
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_U.nc').variables['Ug'][:])))
phase[phase == 18446744073709551616.00000] = 0
URe.append(amp*np.cos(phase*(np.pi/180)))
UIm.append(-amp*np.sin(phase*(np.pi/180)))
URe = np.stack(URe)
UIm = np.stack(UIm)
self.Uvelocity_dataset = [lon_u,lat_u,URe,UIm]
elif grid_type == 'v':
mask = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes'] + constituents[0] + '_V.nc').variables['Va'][:])))
mask[mask != 18446744073709551616.00000] = 1
mask[mask == 18446744073709551616.00000] = 0
self.mask_dataset[mv_name] = mask
VRe = []
VIm = []
lat_v = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_V.nc').variables['lat'][:])
lon_v = np.array(Dataset(settings['tide_fes'] + constituents[0] + '_V.nc').variables['lon'][:])
for ncon in range(len(constituents)):
amp = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Va'][:])))
# set fill value to zero
amp[amp == 18446744073709551616.00000] = 0
# convert amp units to m/s
amp = amp/100.00
phase = np.ma.MaskedArray.filled(np.flipud(np.rot90(Dataset(settings['tide_fes']+constituents[ncon]+'_V.nc').variables['Vg'][:])))
phase[phase == 18446744073709551616.00000] = 0
VRe.append(amp*np.cos(phase*(np.pi/180)))
VIm.append(-amp*np.sin(phase*(np.pi/180)))
VRe = np.stack(VRe)
VIm = np.stack(VIm)
self.Vvelocity_dataset = [lon_v,lat_v,VRe,VIm]
# open grid variables these are resampled TPXO parameters so may not work correctly.
self.grid = Dataset(settings['tide_fes']+'grid_fes.nc')
height_z = np.array(np.rot90(self.grid.variables['hz']))
# set fill values to zero
height_z[height_z <0.00] = 0.00
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[-1, :], ], mask, [mask[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.Uvelocity_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.Vvelocity_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((len(nc_dataset[2]), lon.shape[0]))
gph = np.zeros((len(nc_dataset[2]), lon.shape[0]))
data = np.array(np.ravel(nc_dataset[2]), dtype=complex)
data.imag = np.array(np.ravel(nc_dataset[3]))
data = data.reshape(nc_dataset[2].shape)
#data = data.reshape(1,nc_dataset['M2'][real_var_name].shape)
# data[data==0] = np.NaN
# Lat Lon values
x_values = nc_dataset[0]
y_values = nc_dataset[1]
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.mask_dataset[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)
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)
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')