diff --git a/.gitignore b/.gitignore
index b9e571dfeac4502371741f0d3898316f157bc7b9..8255dbcd46f970101f2a5e54202238ea3d480cae 100644
--- a/.gitignore
+++ b/.gitignore
@@ -33,6 +33,7 @@ wheels/
MANIFEST
outputs
.DS_Store
+DATA
# PyInstaller
# Usually these files are written by a python script from a template
diff --git a/pynemo/tide/fes_extract_HC.py b/pynemo/tide/fes_extract_HC.py
new file mode 100644
index 0000000000000000000000000000000000000000..74037818a1b74a9f2c369c26a9b78dc2c2caff82
--- /dev/null
+++ b/pynemo/tide/fes_extract_HC.py
@@ -0,0 +1,260 @@
+'''
+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 FESExtract(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 = '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 = '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())
+ 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')