from section import Section # fake file just for now fname = fname = 'fake/path/to/netcdf' # set the timestep of intereste nstep = np.arange(0, 2, 1) # variable to plot var = 'salt' # instantiate the Section class, given the i,j-indexes can_clim = Section(fname, 19, 72) # load data for the given transect (in sigma layers) can_clim.load_section(nstep, var, mean=True) # import other variables, as lon,depth,sigma,h1 can_clim.load_auxiliar_data() # create new grid to interpolate can_clim.set_newgrid(1000, 80, 80, 150000) # and finally, interpolate from sigma to z can_clim.interp_sig2z() # plot section can_clim.plot() # save can_clim.savefig(os.path.join(outputdir, 'temp_transect', 'clim_cananeia.png'))
# rectangular section
width = 2.5 # m
height = 0.5 # m
t_spar = 0.05 # m
t_skin = 0.03 # m
Nx = 100
Nz = 20
x = np.linspace(0, width, Nx)
z = np.linspace(0, height, Nz)
lines = [
ThickLine(zip(x, np.zeros(Nx)), t_skin),
ThickLine(zip(width * np.ones(Nz), z), t_spar),
ThickLine(zip(x[::-1], height * np.ones(Nx)), t_skin),
ThickLine(zip(np.zeros(Nz), z[::-1]), t_spar)
]
rectangular_section = Section(lines)
if __name__ == "__main__":
# draw section
fig = rectangular_section.plot()
ax = plt.gca()
ax.set_xlim((-0.5, 3))
ax.set_ylim((-0.25, 1.5))
ann = ax.annotate(r'$I_1 = x$', xy=(1.5, 1), annotation_clip=False)
plt.show()
