def griddata(self, xdata, ydata, zdata, xbin=20, ybin=20):
if self.verbose: print 'Entering griddata...',
xmin = min(xdata)
xmax = max(xdata)
xstep = (xmax - xmin) / xbin
ymin = min(ydata)
ymax = max(ydata)
ystep = (ymax - ymin) / ybin
print xmin, xmax, xstep, ymin, ymax, ystep
xi, yi = mgrid[xmin:xmax:xstep, ymin:ymax:ystep]
# triangulate data
try:
print 'here1'
tri = delaunay.Triangulation(xdata, ydata)
print 'here2'
interp = tri.nn_interpolator(zdata)
print 'here3'
zi = interp(xi, yi)
print 'here4'
except:
print 'Problem in griddata'
if self.verbose: print 'Done'
return xi, yi, zi
def test_ccw_triangles(self):
assert False
for n in (10, 30, 100, 300, 1000, 3000):
x, y = self.rs.uniform(0, 100, size=(2, n))
tri = dlny.Triangulation(x, y)
for i,j,k in tri.triangle_nodes:
assert not onright(x[i], y[i], x[j], y[j], x[k], y[k])
def prep_data2(xt, yt, zorigt, interpolate_on=True):
# Data=pylab.load(r'c:\resolution_stuff\1p4K.iexy')
#Data=pylab.load(filename)
#xt=Data[:,2]
#yt=Data[:,3]
#zorigt=Data[:,0]
xt = N.array(xt).flatten()
yt = N.array(yt).flatten()
zorigt = N.array(zorigt).flatten()
x = xt[:, zorigt > 0.0]
y = yt[:, zorigt > 0.0]
z = zorigt[:, zorigt > 0.0]
# zorig=ma.array(zorigt)
print 'reached'
threshold = 0.0
# print zorigt < threshold
# print N.isnan(zorigt)
# z = ma.masked_where(zorigt < threshold , zorigt)
print 'where masked ', z.shape
#should be commented out--just for testing
## x = pylab.randn(Nu)/aspect
## y = pylab.randn(Nu)
## z = pylab.rand(Nu)
## print x.shape
## print y.shape
# Grid
print x.min()
print x.max()
print y.min()
print y.max()
print x.shape
xmesh_step = 0.02
ymesh_step = 0.5
xi, yi = N.mgrid[x.min():x.max():xmesh_step, y.min():y.max():ymesh_step]
#blah
# triangulate data
tri = D.Triangulation(N.copy(x), N.copy(y))
print 'before interpolator'
# interpolate data
interp = tri.nn_interpolator(z)
print 'interpolator reached'
zi = interp(xi, yi)
# or, all in one line
# zi = Triangulation(x,y).nn_interpolator(z)(xi,yi)
# return x,y,z
if interpolate_on == False:
#print "off"
#print xi.shape
#print N.reshape(x,(15,31))
xi = N.reshape(x, (15, 31))
yi = N.reshape(y, (15, 31))
zi = N.reshape(z, (15, 31))
#print zi2-zi
#blah
print "interpolation off"
return xi, yi, zi
def test_circle_condition(self):
assert False
for n in (10, 30, 100, 300, 1000, 3000):
x, y = self.rs.uniform(0, 100, size=(2, n))
tri = dlny.Triangulation(x, y)
i = tri.triangle_nodes[:,0]
r2 = ((x[i] - tri.circumcenters[:,0])**2
+ (y[i] - tri.circumcenters[:,1])**2)
alldist2 = (sp.subtract.outer(x, tri.circumcenters[:,0])**2
+ sp.subtract.outer(y, tri.circumcenters[:,1])**2)
assert sp.alltrue(r2 <= alldist2)
def test_ccw_hull(self):
assert False
for n in (10, 30, 100, 300, 1000, 3000):
x, y = self.rs.uniform(0, 100, size=(2, n))
tri = dlny.Triangulation(x, y)
hull = list(tri.hull)
hull.append(hull[0])
hull.append(hull[1])
for i,j,k in zip(hull[:-2], hull[1:-1], hull[2:]):
assert not onright(x[i], y[i], x[j], y[j], x[k], y[k])
def test_counts(self):
assert False
for n in (10, 30, 100, 300, 1000, 3000):
x, y = self.rs.uniform(0, 100, size=(2, n))
tri = dlny.Triangulation(x, y)
k = len(tri.hull)
ntriangles = 2*n - 2 - k
nedges = 3*n - 3 - k
assert tri.triangle_nodes.shape == (ntriangles, 3)
assert tri.triangle_neighbors.shape == (ntriangles, 3)
assert tri.edge_db.shape == (nedges, 2)
assert tri.circumcenters.shape == (ntriangles, 2)
assert sp.sum((tri.triangle_neighbors == -1).astype(sp.int32).flat) == k
def interp(self, xt, yt, zorigt):
x = xt[:, zorigt > 0.0]
y = yt[:, zorigt > 0.0]
z = zorigt[:, zorigt > 0.0]
tri = D.Triangulation(x, y)
interp = tri.nn_interpolator(z)
xi = N.copy(x)
yi = N.copy(y)
xi = N.concatenate((xi, self.line_x))
yi = N.concatenate((yi, self.line_y))
zi = interp(xi, yi)
mylocator = locator(xi, yi)
inds = mylocator.inside(self.line_x, self.line_y)
outxi = xi[inds.astype(int)]
outyi = yi[inds.astype(int)]
outzi = zi[inds.astype(int)]
return outxi, outyi, outzi
def extrapolate_mask(a, mask=None):
if mask is None and not isinstance(a, ma.MaskedArray):
return a
if mask is None:
mask = a.mask
else:
if isinstance(a, ma.MaskedArray):
mask = mask | a.mask
a = a[:] # make a copy of array a
jj, ii = indices(a.shape)
igood = ii[~mask]
jgood = jj[~mask]
ibad = ii[mask]
jbad = jj[mask]
tri = delaunay.Triangulation(igood, jgood)
# interpolate from the good points (mask == 1)
interp = tri.nn_extrapolator(a[~mask])
# to the bad points (mask == 0)
a[mask] = interp(ibad, jbad)
return a
def prep_data(filename):
# Data=pylab.load(r'c:\resolution_stuff\1p4K.iexy')
Data = pylab.load(filename)
xt = Data[:, 2]
yt = Data[:, 3]
zorigt = Data[:, 0]
x = xt[:, zorigt > 0.0]
y = yt[:, zorigt > 0.0]
z = zorigt[:, zorigt > 0.0]
# zorig=ma.array(zorigt)
print 'reached'
threshold = 0.0
# print zorigt < threshold
# print N.isnan(zorigt)
# z = ma.masked_where(zorigt < threshold , zorigt)
print 'where masked ', z.shape
#should be commented out--just for testing
## x = pylab.randn(Nu)/aspect
## y = pylab.randn(Nu)
## z = pylab.rand(Nu)
## print x.shape
## print y.shape
# Grid
xi, yi = N.mgrid[-5:5:100j, -5:5:100j]
xi, yi = N.mgrid[x.min():x.max():.05, y.min():y.max():.05]
# triangulate data
tri = D.Triangulation(x, y)
print 'before interpolator'
# interpolate data
interp = tri.nn_interpolator(z)
print 'interpolator reached'
zi = interp(xi, yi)
# or, all in one line
# zi = Triangulation(x,y).nn_interpolator(z)(xi,yi)
# return x,y,z
if interpolate_on == False:
xi = x
yi = y
zi = z
return xi, yi, zi
def transect(x, y, xi, yi, prop):
'''propi = transect(x, y, xi, yi, prop)
Interpolates property prop on a horizontal grid x, y to a
transect given by the points in arrays xi, yi. prop may be
two- or more dimensional, but the two rightmost dimensions must
match those of x and y (which must both be two dimensional). The
grid x, y may be given as a masked array.'''
if ma.isMaskedArray(x) and ma.isMaskedArray(y):
mask = ~x.mask & ~y.mask
else:
mask = ones(x.shape, dtype='bool')
x = x[mask]
y = y[mask]
tri = delaunay.Triangulation(x, y)
if prop.ndim == 2:
return tri.nn_interpolator(prop[mask])(xi, yi)
propi = empty(prop.shape[:-2] + (len(xi), ), 'd')
for idx in zip(*indices(prop.shape[:-2])):
propi[idx, :] = tri.nn_interpolator(prop[idx, mask].flat)(xi, yi)
return propi
def prep_data2(xt, yt, zorigt):
# Data=pylab.load(r'c:\resolution_stuff\1p4K.iexy')
#Data=pylab.load(filename)
#xt=Data[:,2]
#yt=Data[:,3]
#zorigt=Data[:,0]
x = xt[:, zorigt > 0.0]
y = yt[:, zorigt > 0.0]
z = zorigt[:, zorigt > 0.0]
# zorig=ma.array(zorigt)
print 'reached'
threshold = 0.0
# print zorigt < threshold
# print N.isnan(zorigt)
# z = ma.masked_where(zorigt < threshold , zorigt)
print 'where masked ', z.shape
#should be commented out--just for testing
## x = pylab.randn(Nu)/aspect
## y = pylab.randn(Nu)
## z = pylab.rand(Nu)
## print x.shape
## print y.shape
# Grid
print x.min()
print x.max()
print y.min()
print y.max()
print x.shape
xi, yi = N.mgrid[x.min():x.max():1e-4, y.min():y.max():1e-4]
# triangulate data
tri = D.Triangulation(N.copy(x), N.copy(y))
print 'before interpolator'
# interpolate data
interp = tri.nn_interpolator(z)
print 'interpolator reached'
zi = interp(xi, yi)
# or, all in one line
# zi = Triangulation(x,y).nn_interpolator(z)(xi,yi)
# return x,y,z
return xi, yi, zi
if 1:
point1 = (.514, .494)
point2 = (.492, .51)
myline = linegen.linegen(point1, point2)
line_x, line_y = myline.gen_line(divisions=50)
pylab.plot(line_x, line_y, 'red', linewidth=3.0)
ax.set_ylim(ylim)
ax.set_xlim(xlim)
if 1:
xt, yt, zorigt = readmeshfiles(mydirectory, 'dmesh', myend) #0
x = xt[:, zorigt > 0.0]
y = yt[:, zorigt > 0.0]
z = zorigt[:, zorigt > 0.0]
tri = D.Triangulation(x, y)
print 'before last interpolator'
# interpolate data
interp = tri.nn_interpolator(z)
print 'interpolator reached'
xi = N.copy(x)
yi = N.copy(y)
xi = N.concatenate((xi, line_x))
yi = N.concatenate((yi, line_y))
zi = interp(xi, yi)
mylocator = locator.locator(xi, yi)
inds = mylocator.inside(line_x, line_y)
print 'after interpolator'
print inds
print xi[inds.astype(int)]
# data.append(datum)
# print data.get_data()[1].gen_motor1_arr()
# print data.extract_a4()
a3,a4,counts=data.extract_a3a4()
# a3=N.array(a3,'d')
# a4=N.array(a4,'d')
# counts=N.array(counts,'d')
print a3.shape
print a4.shape
print counts.shape
# Grid
# xi, yi = N.mgrid[-5:5:100j,-5:5:100j]
xi,yi=N.mgrid[a3.min():a3.max():.1,a4.min():a4.max():.1]
# triangulate data
tri = D.Triangulation(a3,a4)
# interpolate data
interp = tri.nn_interpolator(counts)
zi = interp(xi,yi)
# or, all in one line
# zi = Triangulation(x,y).nn_interpolator(z)(xi,yi)
plot_data(xi,yi,zi)
# mydatareader.readibuffer(myfilestr)
