mitgcm.visualisation Namespace Reference

Visualisation functions. More...

Functions
def	LIC2_sparse
	Line integral convolution with a sparse noise field. More...
def	LIC2_sparse_animate
	Line integral convolution with a sparse noise field. More...
def	create_cmap_vary_alpha
	Create a colour map with variable alpha. More...

Detailed Description

Visualisation functions.

Each function has a detailed docstring.

Function Documentation

def mitgcm.visualisation.create_cmap_vary_alpha

(

colour = 'white'

)

Create a colour map with variable alpha.

This can be used to sketch out particles as they move.

The only input variable 'coour' defines the colour that is used to create the colour map. It can be any colour code that matplotlib recognises: single letter codes, hex colour string, a standard colour name, or a string representation of a float (e.g. '0.4') for gray on a 0-1 scale.

Definition at line 285 of file visualisation.py.

def mitgcm.visualisation.LIC2_sparse	(		u,
			v,
			points,
			grid_object,
			trace_length,
			kernel = 'anisotropic_linear',
			delta_t = 3600.
	)

Line integral convolution with a sparse noise field.

This produces discrete points that flow around the visualisation.

LIC is a method for visualising flow fields.

---

Parameters

• kernel - the convolution kernel. Options are:
  • 'box'
  • 'anisotropic_linear'

---

Example call

output = LIC2_sparse(m.zonal_velocity['UVEL'][3,:,:],m.meridional_velocity['VVEL'][3,:,:],
                                                    5000,m.grid,15*86400,kernel='anisotropic_linear',delta_t=10*3600)

white_var_alpha = mitgcm.visualisation.create_cmap_vary_alpha(colour='k')

Then plot with

plt.pcolormesh(m.grid['X'][:],m.grid['Yp1'][:],m.meridional_velocity['VVEL'][level,:,:],cmap='winter',vmin=-1,vmax=1)
plt.colorbar()
for i in xrange(output.shape[1]):
    plt.scatter(output[0,i,:],output[1,i,:],
            c=output[2,i,:],cmap=white_var_alpha,lw=0,vmin=0,vmax=1,s=4)

Definition at line 20 of file visualisation.py.

def mitgcm.visualisation.LIC2_sparse_animate	(		u,
			v,
			nparticles,
			grid_object,
			animation_length,
			trace_length,
			kernel = 'anisotropic_linear',
			delta_t = 3600.
	)

Line integral convolution with a sparse noise field.

The sparse noise field produces discrete points that travel around with the flow field.

This function produces data that can be used to animate a static flow field.

LIC is a method for visualising flow fields.

returns:

• output_matrix = [Variables (this axis has three values: x,y,intensity_ramp),trace_number ,time]
• intensity = vector containing the convolved kernel and noise field

---

Parameters

• kernel - the convolution kernel. Options are:
  • 'box' - same intensity for the entire trace
  • 'anisotropic_linear' - intensity ramps up over the trace

---

Example call

output,intensity = LIC2_sparse_animate(m.zonal_velocity['UVEL'][1,:,:],
                                                             m.meridional_velocity['VVEL'][1,:,:],
                                                             15,m.grid,
                                                             300*86400,60*86400,
                                                             kernel='anisotropic_linear',delta_t=4*3600)
white_var_alpha = mitgcm.visualisation.create_cmap_vary_alpha(colour='k')

and then plot with

trace_length= len(intensity)

for t0 in xrange(output.shape[2]-len(intensity)):
    for i in xrange(output.shape[0]):
                plt.scatter(output[i,0,t0:t0+trace_length],output[i,1,t0:t0+trace_length],
                    c = intensity,cmap='winter',lw=0,vmin=0,vmax=1,s=20)

    filename = '../OLIC animation{:04g}.png'.format(t0)
    plt.savefig(filename)
    plt.clf()

Definition at line 120 of file visualisation.py.

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