diff --git a/unit_tests/coord_gen.py b/unit_tests/coord_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..2408da9baa522d63fdb982d346f7be69081820b9
--- /dev/null
+++ b/unit_tests/coord_gen.py
@@ -0,0 +1,377 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 11 11:16:57 2020
+
+example grid set up
+
+@author: jdha
+"""
+
+import numpy as np
+from netCDF4 import Dataset
+
+def set_hgrid(dx, dy, jpi, jpj, zoffx, zoffy):
+ # Set grid positions [km]
+ latt = np.zeros((jpi, jpj))
+ lont = np.zeros((jpi, jpj))
+ lonu = np.zeros((jpi, jpj))
+ latu = np.zeros((jpi, jpj))
+ lonv = np.zeros((jpi, jpj))
+ latv = np.zeros((jpi, jpj))
+ lonf = np.zeros((jpi, jpj))
+ latf = np.zeros((jpi, jpj))
+
+ for i in range(0, jpi):
+ lont[i, :] = zoffx * dx * 1.e-3 + dx * 1.e-3 * np.float(i)
+ lonu[i, :] = zoffx * dx * 1.e-3 + dx * 1.e-3 * (np.float(i) + 0.5)
+
+ for j in range(0, jpj):
+ latt[:, j] = zoffy * dy * 1.e-3 + dy * 1.e-3 * float(j)
+ latv[:, j] = zoffy * dy * 1.e-3 + dy * 1.e-3 * (float(j) + 0.5)
+
+ lonv = lont
+ lonf = lonu
+ latu = latt
+ latf = latv
+
+ e1t = np.ones((jpi, jpj)) * dx
+ e2t = np.ones((jpi, jpj)) * dy
+ e1u = np.ones((jpi, jpj)) * dx
+ e2u = np.ones((jpi, jpj)) * dy
+ e1v = np.ones((jpi, jpj)) * dx
+ e2v = np.ones((jpi, jpj)) * dy
+ e1f = np.ones((jpi, jpj)) * dx
+ e2f = np.ones((jpi, jpj)) * dy
+
+ # Set bathymetry [m]:
+ batt = 500. + 0.5 * 1500. * (1.0 + np.tanh((lont - 40.) / 7.))
+
+ # Set surface mask:
+ ktop = np.zeros((jpi, jpj))
+ #ktop[1:jpi - 1, nghost + 1:jpj - nghost - 1] = 1
+ #batt = np.where((ktop == 0.), 0., batt)
+
+ # Set coriolis parameter:
+ ff_t = np.zeros((jpi, jpj))
+ ff_f = np.zeros((jpi, jpj))
+
+ grid_h = {'lont':lont, 'latt':latt, 'lonu':lonu, 'latu':latu, 'lonv':lonv, 'latv':latv, 'lonf':lonf, 'latf':latf, \
+ 'e1t':e1t, 'e2t':e2t, 'e1u':e1u, 'e2u':e2u, 'e1v':e1v, 'e2v':e2v, 'e1f':e1f, 'e2f':e2f, 'batt':batt, \
+ 'ktop':ktop, 'ff_f':ff_f, 'ff_t':ff_t,'jpi':jpi,'jpj':jpj,'dy':dy,'dx':dx}
+
+ return grid_h
+
+def set_zgrid(grid_h,jpk,max_dep,min_dep,z_dim):
+
+ jpi = grid_h['jpi']
+ jpj = grid_h['jpj']
+
+ dept_1d = np.linspace(min_dep,max_dep,jpk)
+ e3t_1d = np.linspace(1.0,z_dim,jpk)
+ e3w_1d = np.linspace(1.0,z_dim,jpk)
+
+ e3t = np.zeros((jpi, jpj, len(e3t_1d)))
+ e3u = np.zeros((jpi, jpj, len(e3t_1d)))
+ e3v = np.zeros((jpi, jpj, len(e3t_1d)))
+ e3w = np.zeros((jpi, jpj, len(e3w_1d)))
+ e3f = np.zeros((jpi, jpj, len(e3t_1d)))
+ e3uw = np.zeros((jpi, jpj, len(e3t_1d)))
+ e3vw = np.zeros((jpi, jpj, len(e3t_1d)))
+
+ e3t[:] = e3t_1d
+ e3u[:] = e3t_1d
+ e3v[:] = e3t_1d
+ e3w[:] = e3w_1d
+ e3f[:] = e3t_1d
+ e3uw[:] = e3t_1d
+ e3vw[:] = e3t_1d
+
+ grid_z = {'dept_1d':dept_1d,'e3t_1d':e3t_1d,'e3w_1d':e3w_1d,'e3t':e3t,'e3u':e3u,'e3v':e3v,'e3w':e3w,'e3f':e3f, \
+ 'e3uw':e3uw,'e3vw':e3vw}
+
+ return grid_z
+
+def write_coord_H(fileout, grid_h):
+ '''
+ Writes out a NEMO formatted coordinates file.
+
+ Args:
+ fileout (string): filename
+ lon[t/u/v/f](np.ndarray): longitude array at [t/u/v/f]-points (2D)
+ lat[t/u/v/f](np.ndarray): latitude array at [t/u/v/f]-points (2D)
+ e1[t/u/v/f] (np.ndarray): zonal scale factors at [t/u/v/f]-points
+ e2[t/u/v/f] (np.ndarray): meridional scale factors at [t/u/v/f]-points
+
+ Returns:
+ '''
+
+ # Open pointer to netcdf file
+ dataset = Dataset(fileout, 'w', format='NETCDF4_CLASSIC')
+
+ # Get input size and create appropriate dimensions
+ # TODO: add some sort of error handling
+ nx, ny = np.shape(grid_h['lont'])
+ dataset.createDimension('x', nx)
+ dataset.createDimension('y', ny)
+
+ # Create Variables
+ nav_lon = dataset.createVariable('nav_lon', np.float32, ('y', 'x'))
+ nav_lat = dataset.createVariable('nav_lat', np.float32, ('y', 'x'))
+
+ glamt = dataset.createVariable('glamt', np.float64, ('y', 'x'))
+ glamu = dataset.createVariable('glamu', np.float64, ('y', 'x'))
+ glamv = dataset.createVariable('glamv', np.float64, ('y', 'x'))
+ glamf = dataset.createVariable('glamf', np.float64, ('y', 'x'))
+ gphit = dataset.createVariable('gphit', np.float64, ('y', 'x'))
+ gphiu = dataset.createVariable('gphiu', np.float64, ('y', 'x'))
+ gphiv = dataset.createVariable('gphiv', np.float64, ('y', 'x'))
+ gphif = dataset.createVariable('gphif', np.float64, ('y', 'x'))
+
+ ge1t = dataset.createVariable('e1t', np.float64, ('y', 'x'))
+ ge1u = dataset.createVariable('e1u', np.float64, ('y', 'x'))
+ ge1v = dataset.createVariable('e1v', np.float64, ('y', 'x'))
+ ge1f = dataset.createVariable('e1f', np.float64, ('y', 'x'))
+ ge2t = dataset.createVariable('e2t', np.float64, ('y', 'x'))
+ ge2u = dataset.createVariable('e2u', np.float64, ('y', 'x'))
+ ge2v = dataset.createVariable('e2v', np.float64, ('y', 'x'))
+ ge2f = dataset.createVariable('e2f', np.float64, ('y', 'x'))
+
+ nav_lon.units, nav_lon.long_name = 'km', 'X'
+ nav_lat.units, nav_lat.long_name = 'km', 'Y'
+
+ # Populate file with input data
+ # TODO: do we need to transpose?
+ nav_lon[:, :] = grid_h['lont'].T
+ nav_lat[:, :] = grid_h['latt'].T
+
+ glamt[:, :] = grid_h['lont'].T
+ glamu[:, :] = grid_h['lonu'].T
+ glamv[:, :] = grid_h['lonv'].T
+ glamf[:, :] = grid_h['lonf'].T
+ gphit[:, :] = grid_h['latt'].T
+ gphiu[:, :] = grid_h['latu'].T
+ gphiv[:, :] = grid_h['latv'].T
+ gphif[:, :] = grid_h['latf'].T
+
+ ge1t[:, :] = grid_h['e1t'].T
+ ge1u[:, :] = grid_h['e1u'].T
+ ge1v[:, :] = grid_h['e1v'].T
+ ge1f[:, :] = grid_h['e1f'].T
+ ge2t[:, :] = grid_h['e2t'].T
+ ge2u[:, :] = grid_h['e2u'].T
+ ge2v[:, :] = grid_h['e2v'].T
+ ge2f[:, :] = grid_h['e2f'].T
+
+ # Close off pointer
+ dataset.close()
+
+ return 0
+
+def write_coord_Z(fileout, grid_h,grid_z):
+ '''
+ Writes out a NEMO formatted coordinates file.
+
+ Args:
+
+ Returns:
+ '''
+
+ # Open pointer to netcdf file
+ dataset = Dataset(fileout, 'w', format='NETCDF4_CLASSIC')
+
+ # Get input size and create appropriate dimensions
+ # TODO: add some sort of error handling
+ nx, ny, nz = np.shape(grid_z['e3t'])
+ dataset.createDimension('x', nx)
+ dataset.createDimension('y', ny)
+ dataset.createDimension('z', nz)
+
+
+ # Create Variables
+ nav_lon = dataset.createVariable('nav_lon', np.float32, ('y', 'x'))
+ nav_lat = dataset.createVariable('nav_lat', np.float32, ('y', 'x'))
+ nav_lev = dataset.createVariable('nav_lev', np.float32, 'z')
+
+ ge3t1d = dataset.createVariable('e3t_1d', np.float64, 'z')
+ ge3w1d = dataset.createVariable('e3w_1d', np.float64, 'z')
+ gbat = dataset.createVariable('mbathy', np.float64, ('y', 'x'))
+ ge3t = dataset.createVariable('e3t', np.float64, ('z','y', 'x'))
+ ge3u = dataset.createVariable('e3u', np.float64, ('z','y', 'x'))
+ ge3v = dataset.createVariable('e3v', np.float64, ('z','y', 'x'))
+ ge3f = dataset.createVariable('e3f', np.float64, ('z','y', 'x'))
+ ge3uw = dataset.createVariable('e3uw', np.float64, ('z', 'y', 'x'))
+ ge3vw = dataset.createVariable('e3vw', np.float64, ('z', 'y', 'x'))
+
+ nav_lon.units, nav_lon.long_name = 'km', 'X'
+ nav_lat.units, nav_lat.long_name = 'km', 'Y'
+ nav_lev.units, nav_lev.long_name = 'm', 'Z'
+
+ # Populate file with input data
+ # TODO: do we need to transpose?
+ nav_lon[:, :] = grid_h['lont'].T
+ nav_lat[:, :] = grid_h['latt'].T
+ nav_lev[:] = grid_z['dept_1d']
+
+ ge3t[:, :, :] = grid_z['e3t'].T
+ ge3u[:, :, :] = grid_z['e3u'].T
+ ge3v[:, :, :] = grid_z['e3v'].T
+ ge3f[:, :, :] = grid_z['e3f'].T
+ ge3uw[:, :, :] = grid_z['e3uw'].T
+ ge3vw[:, :, :] = grid_z['e3vw'].T
+
+ ge3t1d[:] = grid_z['e3t_1d']
+ ge3w1d[:] = grid_z['e3w_1d']
+ gbat[:,:] = grid_h['batt'].T
+
+
+ # Close off pointer
+ dataset.close()
+
+ return 0
+
+def write_domcfg(fileout, ln_zco, ln_zps, ln_sco, ln_isfcav, jperio, bat,
+ lont, latt, lonu, latu, lonv, latv, lonf, latf,
+ e1t, e2t, e1u, e2u, e1v, e2v, e1f, e2f, ff_f, ff_t,
+ dept_1d, e3t_1d, e3w_1d, e3t, e3u, e3v, e3f, e3w, e3uw, e3vw,
+ ktop, kbot):
+ '''
+ Writes out a NEMO formatted domcfg file.
+
+ Args:
+ fileout (string): filename
+ ln_zco (logical): vertical coordinate flag [z-level]
+ ln_zps (logical): vertical coordinate flag [z-partial-step]
+ ln_sco (logical): vertical coordinate flag [sigma]
+ ln_isfcav (logical): ice cavity flag
+ jperio (int): domain type
+ bat (np.ndarray): bathymetry array at t-points (2D)
+ lon[t/u/v/f](np.ndarray): longitude array at [t/u/v/f]-points (2D)
+ lat[t/u/v/f](np.ndarray): latitude array at [t/u/v/f]-points (2D)
+ e1[t/u/v/f] (np.ndarray): zonal scale factors at [t/u/v/f]-points
+ e2[t/u/v/f] (np.ndarray): meridional scale factors at [t/u/v/f]-points
+ ff_[f/t] (np.ndarray): coriolis parameter at [t/f]-points
+ dept_1d (np.ndarray): 1D depth levels at t-points
+ e3[t/w]_1d (np.ndarray): 1D vertical scale factors at [t/w]-points
+ e3[t/u/v/f] (np.ndarray): vertcal scale factors at [t/u/v/f]-points
+ e3[w/uw/vw] (np.ndarray): vertcal scale factors at [w/uw/vw]-points
+ ktop (np.ndarray): upper most wet point
+ kbot (np.ndarray): lower most wet point
+
+ Returns:
+ '''
+
+ # Open pointer to netcdf file
+ dataset = Dataset(fileout, 'w', format='NETCDF4_CLASSIC')
+
+ # Get input size and create appropriate dimensions
+ # TODO: add some sort of error handling
+ nx, ny, nz = np.shape(e3t)
+ dataset.createDimension('x', nx)
+ dataset.createDimension('y', ny)
+ dataset.createDimension('z', nz)
+
+ # create Variables
+ nav_lon = dataset.createVariable('nav_lon', np.float32, ('y', 'x'))
+ nav_lat = dataset.createVariable('nav_lat', np.float32, ('y', 'x'))
+ nav_lev = dataset.createVariable('nav_lev', np.float32, 'z')
+
+ giglo = dataset.createVariable('jpiglo', "i4")
+ gjglo = dataset.createVariable('jpjglo', "i4")
+ gkglo = dataset.createVariable('jpkglo', "i4")
+
+ gperio = dataset.createVariable('jperio', "i4")
+
+ gzco = dataset.createVariable('ln_zco', "i4")
+ gzps = dataset.createVariable('ln_zps', "i4")
+ gsco = dataset.createVariable('ln_sco', "i4")
+ gcav = dataset.createVariable('ln_isfcav', "i4")
+
+ ge3t1d = dataset.createVariable('e3t_1d', np.float64, 'z')
+ ge3w1d = dataset.createVariable('e3w_1d', np.float64, 'z')
+ gitop = dataset.createVariable('top_level', "i4", ('y', 'x'))
+ gibot = dataset.createVariable('bottom_level', "i4", ('y', 'x'))
+ gbat = dataset.createVariable('Bathymetry', np.float64, ('y', 'x'))
+ glamt = dataset.createVariable('glamt', np.float64, ('y', 'x'))
+ glamu = dataset.createVariable('glamu', np.float64, ('y', 'x'))
+ glamv = dataset.createVariable('glamv', np.float64, ('y', 'x'))
+ glamf = dataset.createVariable('glamf', np.float64, ('y', 'x'))
+ gphit = dataset.createVariable('gphit', np.float64, ('y', 'x'))
+ gphiu = dataset.createVariable('gphiu', np.float64, ('y', 'x'))
+ gphiv = dataset.createVariable('gphiv', np.float64, ('y', 'x'))
+ gphif = dataset.createVariable('gphif', np.float64, ('y', 'x'))
+ ge1t = dataset.createVariable('e1t', np.float64, ('y', 'x'))
+ ge1u = dataset.createVariable('e1u', np.float64, ('y', 'x'))
+ ge1v = dataset.createVariable('e1v', np.float64, ('y', 'x'))
+ ge1f = dataset.createVariable('e1f', np.float64, ('y', 'x'))
+ ge2t = dataset.createVariable('e2t', np.float64, ('y', 'x'))
+ ge2u = dataset.createVariable('e2u', np.float64, ('y', 'x'))
+ ge2v = dataset.createVariable('e2v', np.float64, ('y', 'x'))
+ ge2f = dataset.createVariable('e2f', np.float64, ('y', 'x'))
+ gfff = dataset.createVariable('ff_f', np.float64, ('y', 'x'))
+ gfft = dataset.createVariable('ff_t', np.float64, ('y', 'x'))
+ ge3t = dataset.createVariable('e3t_0', np.float64, ('z', 'y', 'x'))
+ ge3w = dataset.createVariable('e3w_0', np.float64, ('z', 'y', 'x'))
+ ge3u = dataset.createVariable('e3u_0', np.float64, ('z', 'y', 'x'))
+ ge3v = dataset.createVariable('e3v_0', np.float64, ('z', 'y', 'x'))
+ ge3f = dataset.createVariable('e3f_0', np.float64, ('z', 'y', 'x'))
+ ge3uw = dataset.createVariable('e3uw_0', np.float64, ('z', 'y', 'x'))
+ ge3vw = dataset.createVariable('e3vw_0', np.float64, ('z', 'y', 'x'))
+
+ nav_lon.units, nav_lon.long_name = 'km', 'X'
+ nav_lat.units, nav_lat.long_name = 'km', 'Y'
+
+ # Populate file with input data
+ giglo[:] = nx
+ gjglo[:] = ny
+ gkglo[:] = nz
+
+ gzco[:] = ln_zco
+ gzps[:] = ln_zps
+ gsco[:] = ln_sco
+ gcav[:] = ln_isfcav
+
+ gperio[:] = jperio
+
+ # TODO: do we need to transpose?
+ nav_lon[:, :] = lont.T
+ nav_lat[:, :] = latt.T
+ nav_lev[:] = dept_1d
+
+ ge3t1d[:] = e3t_1d
+ ge3w1d[:] = e3w_1d
+
+ gitop[:, :] = ktop.T
+ gibot[:, :] = kbot.T
+
+ gbat[:, :] = bat.T
+
+ glamt[:, :] = lont.T
+ glamu[:, :] = lonu.T
+ glamv[:, :] = lonv.T
+ glamf[:, :] = lonf.T
+ gphit[:, :] = latt.T
+ gphiu[:, :] = latu.T
+ gphiv[:, :] = latv.T
+ gphif[:, :] = latf.T
+
+ ge1t[:, :] = e1t.T
+ ge1u[:, :] = e1u.T
+ ge1v[:, :] = e1v.T
+ ge1f[:, :] = e1f.T
+ ge2t[:, :] = e2t.T
+ ge2u[:, :] = e2u.T
+ ge2v[:, :] = e2v.T
+ ge2f[:, :] = e2f.T
+ gfff[:, :] = ff_f.T
+ gfft[:, :] = ff_t.T
+
+ ge3t[:, :, :] = e3t.T
+ ge3w[:, :, :] = e3w.T
+ ge3u[:, :, :] = e3u.T
+ ge3v[:, :, :] = e3v.T
+ ge3f[:, :, :] = e3f.T
+ ge3uw[:, :, :] = e3uw.T
+ ge3vw[:, :, :] = e3vw.T
+
+ # Close off pointer
+ dataset.close()
diff --git a/unit_tests/rotate_pts.py b/unit_tests/rotate_pts.py
index 3541b65cc9565b9786578330d4e66d9cdaad7b09..24882e71eddb08893e64ffeee7a90330c705e13e 100644
--- a/unit_tests/rotate_pts.py
+++ b/unit_tests/rotate_pts.py
@@ -6,6 +6,7 @@ occurs, the results are then plotted in a figure showing original (blue points)
The source coordinate grid is also plotted (green).
"""
+
from math import cos,sin,radians
import numpy as np
import xarray as xr
@@ -13,6 +14,8 @@ import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
+import unit_tests.coord_gen as cg
+
# TODO: add in auto max and min lat and lon
# TODO: remove hard coded file names and directories.
@@ -40,37 +43,37 @@ def rotate_around_point(lat_in,lon_in, radians , origin=(0, 0)):
def plot_grids(lat_in,lon_in,new_lat,new_lon,src_lat,src_lon):
# define lat and lon extents (define in future using input values?)
- maxlat = 72
- minlat = 32
- maxlon = 18
- minlon = -28
+ #maxlat = 72
+ #minlat = 32
+ #maxlon = 18
+ #minlon = -28
plt.figure(figsize=[18, 8]) # a new figure window
- ax = plt.subplot(121, projection=ccrs.PlateCarree()) # specify (nrows, ncols, axnum)
- ax.set_extent([minlon,maxlon,minlat,maxlat],crs=ccrs.PlateCarree()) #set extent
- ax.set_title('Original Grid', fontsize=20,y=1.05) #set title
- ax.add_feature(cartopy.feature.LAND, zorder=0) # add land polygon
- ax.add_feature(cartopy.feature.COASTLINE, zorder=10) # add coastline polyline
- gl = ax.gridlines(crs=ccrs.PlateCarree(), linewidth=2, color='black', alpha=0.5, linestyle='--', draw_labels=True)
+ ax = plt.subplot(121)#, projection=ccrs.PlateCarree()) # specify (nrows, ncols, axnum)
+ #ax.set_extent([minlon,maxlon,minlat,maxlat],crs=ccrs.PlateCarree()) #set extent
+ #ax.set_title('Original Grid', fontsize=20,y=1.05) #set title
+ #ax.add_feature(cartopy.feature.LAND, zorder=0) # add land polygon
+ #ax.add_feature(cartopy.feature.COASTLINE, zorder=10) # add coastline polyline
+ #gl = ax.gridlines(crs=ccrs.PlateCarree(), linewidth=2, color='black', alpha=0.5, linestyle='--', draw_labels=True)
- ax1 = plt.subplot(122, projection=ccrs.PlateCarree()) # specify (nrows, ncols, axnum)
- ax1.set_extent([minlon,maxlon,minlat,maxlat],crs=ccrs.PlateCarree()) #set extent
- ax1.set_title('Rotated Grid', fontsize=20,y=1.05) #set title
- ax1.add_feature(cartopy.feature.LAND, zorder=0) # add land polygon
- ax1.add_feature(cartopy.feature.COASTLINE, zorder=10) # add coastline polyline
- gl1 = ax1.gridlines(crs=ccrs.PlateCarree(), linewidth=2, color='black', alpha=0.5, linestyle='--', draw_labels=True)
+ ax1 = plt.subplot(122)#, projection=ccrs.PlateCarree()) # specify (nrows, ncols, axnum)
+ #ax1.set_extent([minlon,maxlon,minlat,maxlat],crs=ccrs.PlateCarree()) #set extent
+ #ax1.set_title('Rotated Grid', fontsize=20,y=1.05) #set title
+ #ax1.add_feature(cartopy.feature.LAND, zorder=0) # add land polygon
+ #ax1.add_feature(cartopy.feature.COASTLINE, zorder=10) # add coastline polyline
+ #gl1 = ax1.gridlines(crs=ccrs.PlateCarree(), linewidth=2, color='black', alpha=0.5, linestyle='--', draw_labels=True)
# tile 1D lat and lon to 2D arrays for plotting (src lat and lon only)
- src_lon = np.tile(src_lon, (np.shape(src_lat)[0], 1))
- src_lat = np.tile(src_lat, (np.shape(src_lon)[1], 1))
- src_lat = np.rot90(src_lat)
+ #src_lon = np.tile(src_lon, (np.shape(src_lat)[0], 1))
+ #src_lat = np.tile(src_lat, (np.shape(src_lon)[1], 1))
+ #src_lat = np.rot90(src_lat)
# plot lat and lon for all grids
- ax.plot(lon_in, lat_in, color='blue', marker='.')
- ax.plot(src_lon,src_lat, color='green', marker='.')
- ax1.plot(new_lon,new_lat, color='red', marker='.')
- ax1.plot(src_lon,src_lat, color='green',marker='.')
+ ax.plot(lon_in, lat_in, color='blue', marker='.', linestyle="")
+ ax.plot(src_lon,src_lat, color='green', marker='o', linestyle="")
+ ax1.plot(new_lon,new_lat, color='red', marker='.', linestyle="")
+ ax1.plot(src_lon,src_lat, color='green',marker='o',linestyle="")
# tweak margins of subplots as tight layout doesn't work
plt.subplots_adjust(left=0.01, right=1, top=0.9, bottom=0.05,wspace=0.01)
@@ -78,24 +81,62 @@ def plot_grids(lat_in,lon_in,new_lat,new_lon,src_lat,src_lon):
def _main():
- #open source (parent) and destination (child) grids
- ds = xr.open_dataset('unit_tests/test_data/dst_hgr_zps.nc')
- src = xr.open_dataset('unit_tests/test_data/src_coordinates.nc')
+ #Source Coords
+ dx = 2000 # units in km
+ dy = 2000 # units in Km
+ jpi = 10
+ jpj = 10
+ zoffx = 0
+ zoffy = 0
+ jpk = 10
+ max_dep = 100
+ min_dep = 10
+ z_end_dim = 10.0
+ h_fname = 'unit_tests/test_data/test_src__hgr_zps.nc'
+ z_fname = 'unit_tests/test_data/test_src_zgr_zps.nc'
+ grid_h1 = cg.set_hgrid(dx,dy,jpi,jpj,zoffx,zoffy)
+ grid_z1 = cg.set_zgrid(grid_h1,jpk,max_dep,min_dep,z_end_dim)
+ write_coord_H = cg.write_coord_H(h_fname,grid_h1)
+ write_coord_Z = cg.write_coord_Z(z_fname,grid_h1,grid_z1)
+ if write_coord_H + write_coord_Z == 0:
+ print("Success!")
+
+ #Dst Coords
+ dx = 100 # units in km
+ dy = 100 # units in Km
+ jpi = 90
+ jpj = 90
+ zoffx = 0
+ zoffy = 0
+ jpk = 10
+ max_dep = 100
+ min_dep = 10
+ z_end_dim = 10.0
+ h_fname = 'unit_tests/test_data/test_dst_hgr_zps.nc'
+ z_fname = 'unit_tests/test_data/test_dst_zgr_zps.nc'
+ grid_h2 = cg.set_hgrid(dx,dy,jpi,jpj,zoffx,zoffy)
+ grid_z2 = cg.set_zgrid(grid_h2,jpk,max_dep,min_dep,z_end_dim)
+ write_coord_H = cg.write_coord_H(h_fname,grid_h2)
+ write_coord_Z = cg.write_coord_Z(z_fname,grid_h2,grid_z2)
+ if write_coord_H + write_coord_Z == 0:
+ print("Success!")
+
# set rotation and origin point
rot = 45
theta = radians(rot)
- origin = (54, -6)
- # generate new lat and lon
- new_lat,new_lon = rotate_around_point(ds.nav_lat.values[:],ds.nav_lon.values[:],theta,origin)
+ origin = (zoffx,zoffy)
+
+
+ new_lat,new_lon = rotate_around_point(grid_h2['latt'],grid_h2['lont'],theta,origin)
# plot orginal, rotatated and source lat and lon
- plot_grids(ds.nav_lat.values[:],ds.nav_lon.values[:],new_lat,new_lon,src.latitude.values[:],src.longitude.values[:])
+ plot_grids(grid_h2['latt'],grid_h2['lont'],new_lat,new_lon,grid_h1['latt'],grid_h1['lont'])
# save new lat lon to dataset
- ds.nav_lat.values[:], ds.nav_lon.values[:] = new_lat,new_lon
+ #ds.nav_lat.values[:], ds.nav_lon.values[:] = new_lat,new_lon
# save dataset to netcdf
- ds.to_netcdf('unit_tests/test_data/rot_'+str(rot)+'_dst_hgr_zps.nc')
+ #ds.to_netcdf('unit_tests/test_data/rot_'+str(rot)+'_dst_hgr_zps.nc')
# close data files
- ds.close()
- src.close()
+ #ds.close()
+ #src.close()
if __name__ == '__main__':
_main()
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