def test_tracer():
from kaw_analysis import test_vcalc as tv#{{{
N = 64
dta = tv.sin_cos_prod(N, 5, 5)
# dta = psi_arrs[-1]
dd = field_trace.Derivator(dta, dta.shape[0], dta.shape[1])
nulls = field_trace.find_null_cells(dd)
saddles = [null for null in nulls if null.is_saddle()]
peaks = [null for null in nulls if not null.is_saddle()]
error = 0.0
for null in nulls:
error += abs(dd.perp_deriv1_interp.eval(null.x0, null.y0))
error += abs(dd.perp_deriv2_interp.eval(null.x0, null.y0))
error /= len(nulls)
saddle0s = [(s.x0, s.y0) for s in saddles]
peak0s = [(p.x0, p.y0) for p in peaks]
# finished, traces = field_trace.make_skeleton(
# arr=dta, deriv=dd, saddles=saddles, ncomb=1, start_offset=1.e-3, hit_radius=1.e-3)
traces = []
for saddle in saddles:
for outgoing in (True, False):
for sgn in (1,-1):
trace = field_trace.trace_from_saddle(
arr=dta, deriv=dd, start_saddle=saddle,
start_offset=sgn*1.e-2, outgoing=outgoing, timeout=0.001)
traces.append(trace)
if 1:
import pylab as pl
pl.ion()
pl.imshow(dta, cmap='hot')
for tr in traces:
tr = np.array(tr)
for i in range(0, len(tr[0]), 2):
X,Y = tr[:,i], tr[:,i+1]
pl.scatter(Y, X, c='r')
# Plot the grid points
if 0:
X = np.linspace(0, dta.shape[0]-1, dta.shape[0])
for i in range(dta.shape[0]):
Y = np.zeros(dta.shape[1])
Y.fill(i)
pl.scatter(Y, X, c='m')
X, Y = zip(*saddle0s)
pl.scatter(Y, X, c='k')
X, Y = zip(*peak0s)
pl.scatter(Y, X, c='b')
import pdb; pdb.set_trace()#}}}
def _test_find_null_2x2():
class _dummy(object): pass
dd = _dummy()
oeigsystem = field_trace.eigsystem
try:
field_trace.eigsystem = lambda x,y,z: (None, None)
dd.perp_deriv1 = np.array([[-2.,-2.],[1.,1.]])
dd.perp_deriv2 = np.array([[-1., 2.],[-1., 2]])
null = field_trace.find_null_cells(dd)[0]
ok_(np.allclose((null.x0, null.y0), (2./3, 1./3)))
dd.perp_deriv1 = np.array([[-1.,-1.],[1.,1.]])
dd.perp_deriv2 = np.array([[-1., 1.],[-1., 1.]])
null = field_trace.find_null_cells(dd)[0]
ok_(np.allclose((null.x0, null.y0), (1./2, 1./2)))
finally:
field_trace.eigsystem = oeigsystem
def test_level_set():
N = 64
n, m = 1, 4
test_data = tv.sin_cos_arr(N, n, m)
dd = field_trace.Derivator(test_data)
nulls = field_trace.find_null_cells(dd)
saddles = [null for null in nulls if null.is_saddle()]
peaks = [null for null in nulls if not null.is_saddle()]
saddle0s = [(s.x0, s.y0) for s in saddles]
peak0s = [(p.x0, p.y0) for p in peaks]
x0, y0 = saddle0s[0]
psi_interp = Interp2DPeriodic(N, N, test_data)
level_val = psi_interp.eval(x0, y0)
level_sets = [null.levelset for null in nulls]
mask = field_trace.marked_to_mask(test_data.shape, level_sets)
masked_data = test_data.copy()
masked_data[mask] = test_data.max()
if 0:
import pylab as pl
pl.ion()
pl.imshow(masked_data, cmap='hot', interpolation='nearest')
# # Plot the grid points
# if 0:
# X = np.linspace(0, dta.shape[0]-1, dta.shape[0])
# for i in range(dta.shape[0]):
# Y = np.zeros(dta.shape[1])
# Y.fill(i)
# pl.scatter(Y, X, c='m')
X, Y = zip(*saddle0s)
pl.scatter(Y, X, c='k')
X, Y = zip(*peak0s)
pl.scatter(Y, X, c='b')
raw_input("enter to continue")
def test_level_set():
# N = 128#{{{
# n, m = 10, 15
# test_data = tv.sin_cos_arr(N, n, m)
arr_idx = -2
test_data = psi_arrs[arr_idx].astype(np.double)
N = test_data.shape[0]
print("locating nulls")
dd = field_trace.Derivator(test_data, N, N)
nulls = field_trace.find_null_cells(dd)
null0s = [(n.x0, n.y0) for n in nulls]
psi_interp = Interp2DPeriodic(N, N, test_data)
print("computing level sets")
levels = [null.levelset for null in nulls]
print("classifying nulls")
peaks = []
saddles = []
for null in nulls:
if null.is_saddle():
saddles.append(null)
else:
peaks.append(null)
peak0s = [(p.x0, p.y0) for p in peaks]
saddle0s = [(s.x0, s.y0) for s in saddles]
print( "getting min regions")
regions = []
for null in nulls:
regions.extend(null.regions)
print( "number of regions: %d" % len(regions))
min_regions = field_trace.filter_min_regions(regions)
print( "number of min regions: %d" % len(min_regions))
print( "plotting")
if 1:
import pylab as pl
pl.ion()
pl.figure()
dta = np.zeros(test_data.shape, dtype=np.int32)
for min_region in min_regions:
dta[min_region.xs, min_region.ys] = 1
pl.imshow(dta, interpolation='nearest')
pl.figure()
pl.imshow(cur_arrs[arr_idx])
pl.figure()
pl.imshow(psi_arrs[arr_idx])
raw_input("enter to continue")
if 0:
import pylab as pl
pl.ion()
all_masks = field_trace.marked_to_mask(test_data.shape, levels)
masked_data = test_data.copy()
masked_data[all_masks] = test_data.max()
pl.imshow(masked_data, cmap='hot', interpolation='nearest')
X, Y = zip(*peak0s)
pl.scatter(Y, X, c='k')
X, Y = zip(*saddle0s)
pl.scatter(Y, X, c='b')
if 0:
for level in levels:
masked_data = test_data.copy()
mask = field_trace.marked_to_mask(test_data.shape, [level])
masked_data[mask] = test_data.max()
pl.imshow(masked_data, cmap='hot', interpolation='nearest')
X, Y = zip(*null0s)
pl.scatter(Y, X, c='k')
X, Y = zip(*null0s)
pl.scatter(Y, X, c='b')
raw_input("enter to continue")#}}}