Ignore:
Timestamp:
Mar 31, 2012, 2:47:12 PM (13 years ago)
Author:
davies
Message:

balanced_dev: fixed stack overflow problem, & new 'transect plot'
routine in util.py

File:
1 edited

Legend:

Unmodified
Added
Removed
  • trunk/anuga_work/development/gareth/tests/util.py

    r8353 r8375  
    7373##############
    7474
    75 def animate_1D(time, var, x, ylab=' '): #, x=range(var.shape[1]), vmin=var.min(), vmax=var.max()):
    76     # Input: time = one-dimensional time vector;
    77     #        var =  array with first dimension = len(time) ;
    78     #        x = (optional) vector width dimension equal to var.shape[1];
    79    
    80     import pylab
    81     import numpy
    82    
    83    
    84 
    85     pylab.close()
    86     pylab.ion()
    87 
    88     # Initial plot
    89     vmin=var.min()
    90     vmax=var.max()
    91     line, = pylab.plot( (x.min(), x.max()), (vmin, vmax), 'o')
    92 
    93     # Lots of plots
    94     for i in range(len(time)):
    95         line.set_xdata(x)
    96         line.set_ydata(var[i,:])
    97         pylab.draw()
    98         pylab.xlabel('x')
    99         pylab.ylabel(ylab)
    100         pylab.title('time = ' + str(time[i]))
    101    
    102     return
    10375
    10476
     
    198170#pylab.quiver(x,y,xvel[n,:],yvel[n,:])
    199171#pylab.savefig('Plot1.png')
     172
     173def animate_1D(time, var, x, ylab=' '): #, x=range(var.shape[1]), vmin=var.min(), vmax=var.max()):
     174    # Input: time = one-dimensional time vector;
     175    #        var =  array with first dimension = len(time) ;
     176    #        x = (optional) vector width dimension equal to var.shape[1];
     177   
     178    import pylab
     179    import numpy
     180   
     181   
     182
     183    pylab.close()
     184    pylab.ion()
     185
     186    # Initial plot
     187    vmin=var.min()
     188    vmax=var.max()
     189    line, = pylab.plot( (x.min(), x.max()), (vmin, vmax), 'o')
     190
     191    # Lots of plots
     192    for i in range(len(time)):
     193        line.set_xdata(x)
     194        line.set_ydata(var[i,:])
     195        pylab.draw()
     196        pylab.xlabel('x')
     197        pylab.ylabel(ylab)
     198        pylab.title('time = ' + str(time[i]))
     199   
     200    return
     201
     202def near_transect(p, point1, point2, tol=1.):
     203    # Function to get the indices of points in p less than 'tol' from the line
     204    # joining (x1,y1), and (x2,y2)
     205    # p comes from util.get_output('mysww.sww')
     206    #
     207    # e.g.
     208    # import util
     209    # #import transect_interpolate
     210    # from matplotlib import pyplot
     211    # p=util.get_output('merewether_1m.sww',0.01)
     212    # p2=util.get_centroids(p,velocity_extrapolation=True)
     213    # #xxx=transect_interpolate.near_transect(p,[95., 85.], [120.,68.],tol=2.)
     214    # xxx=util.near_transect(p,[95., 85.], [120.,68.],tol=2.)
     215    # pyplot.scatter(xxx[1],p.vel[140,xxx[0]],color='red')
     216
     217    x1=point1[0]
     218    y1=point1[1]
     219
     220    x2=point2[0]
     221    y2=point2[1]
     222
     223    # Find line equation a*x + b*y + c = 0
     224    # based on y=gradient*x +intercept
     225    if x1!=x2:
     226        gradient= (y2-y1)/(x2-x1)
     227        intercept = y1 - gradient*x1
     228
     229        a = -gradient
     230        b = 1.
     231        c = -intercept
     232    else:
     233        #print 'FIXME: Still need to treat 0 and infinite gradients'
     234        #assert 0==1
     235        a=1.
     236        b=0.
     237        c=-x2
     238
     239    # Distance formula
     240    inv_denom = 1./(a**2 + b**2)**0.5
     241    distp = abs(p.x*a + p.y*b + c)*inv_denom
     242
     243    near_points = (distp<tol).nonzero()[0]
     244   
     245    # Now find a 'local' coordinate for the point, projected onto the line
     246    # g1 = unit vector parallel to the line
     247    # g2 = vector joining (x1,y1) and (p.x,p.y)
     248    g1x = x2-x1
     249    g1y = y2-y1
     250    g1_norm = (g1x**2 + g1y**2)**0.5
     251    g1x=g1x/g1_norm
     252    g1y=g1x/g1_norm
     253
     254    g2x = p.x[near_points] - x1
     255    g2y = p.y[near_points] - y1
     256   
     257    # Dot product = projected distance == a local coordinate
     258    local_coord = g1x*g2x + g1y*g2y
     259
     260    return near_points, local_coord
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