def tester(n, m):
N = global_N
test_data = tv.sin_cos_prod(N, n, m)
from upsample import upsample
test_data = upsample(test_data, factor=4)
dd = field_trace.Derivator(test_data)
nulls = field_trace.find_null_cells_minimize(dd)
null_locs = [(null.x0, null.y0) for null in nulls]
saddles = [_ for _ in nulls if _.is_saddle()]
maxs = [_ for _ in nulls if _.is_maximum()]
mins = [_ for _ in nulls if _.is_minimum()]
eq_(len(saddles + maxs + mins), len(nulls))
print ("4*n*m: %d num_saddles: %d num_max+num_min: %d" %
(4*n*m, len(saddles), len(maxs+mins)))
if 1:
import pylab as pl
pl.ion()
pl.imshow(test_data)
X = [_.x0 for _ in saddles]
Y = [_.y0 for _ in saddles]
pl.scatter(Y, X, marker='x', c='k')
X = [_.x0 for _ in maxs]
Y = [_.y0 for _ in maxs]
pl.scatter(Y, X, c='r', marker='d')
X = [_.x0 for _ in mins]
Y = [_.y0 for _ in mins]
pl.scatter(Y, X, c='b', marker='o')
raw_input("enter to continue")
pl.close('all')
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_upsample():
upsample_factor = 4
N = global_N
test_data = tv.sin_cos_prod(N, N/4, N/8)
test_data_4 = tv.sin_cos_prod(N*4, N/4, N/8)
upsampled = upsample.upsample(test_data, factor=4)
ok_(np.allclose(upsample_factor**2 * upsampled, test_data_4))
if 0:
pl.ion()
pl.imshow(test_data, interpolation='nearest', cmap='hot')
pl.title('original data')
pl.figure()
pl.imshow(upsampled, interpolation='nearest', cmap='hot')
pl.title('upsampled by %d' % upsample_factor)
pl.figure()
pl.imshow(test_data_4, interpolation='nearest', cmap='hot')
pl.title('comparison')
raw_input('enter to continue')
def tester(n, m):
N = global_N
test_data = tv.sin_cos_prod(N, n, m)
# from upsample import upsample
# test_data = upsample(test_data, factor=4)
dd = field_trace.Derivator(test_data)
arr1 = np.zeros((N, N), dtype=np.double)
arr2 = np.zeros((N, N), dtype=np.double)
for i in range(N):
for j in range(N):
evals, evecs = field_trace.hessian_esys(dd, i, j)
arr1[i,j] = min(evals)
arr2[i,j] = max(evals)
nulls = field_trace.find_null_cells_minimize(dd)
null_locs = [(null.x0, null.y0) for null in nulls]
saddles = [_ for _ in nulls if _.is_saddle()]
maxs = [_ for _ in nulls if _.is_maximum()]
mins = [_ for _ in nulls if _.is_minimum()]
saddleXs, saddleYs = [_.x0 for _ in saddles], [_.y0 for _ in saddles]
minXs, minYs = [_.x0 for _ in mins], [_.y0 for _ in mins]
maxXs, maxYs = [_.x0 for _ in maxs], [_.y0 for _ in maxs]
if 0:
def ims(x):
pl.imshow(x, interpolation='nearest', cmap='hot')
pl.scatter(saddleYs, saddleXs, marker='x', c='k')
pl.scatter(minYs, minXs, marker='o', c='b')
pl.scatter(maxYs, maxXs, marker='d', c='r')
import pylab as pl
pl.ion()
# ims(dd.deriv11)
# pl.figure()
# ims(dd.deriv22)
# pl.figure()
# ims(dd.deriv12)
# pl.figure()
ims(test_data)
pl.figure()
ims(arr1)
pl.figure()
ims(arr2)
pl.figure()
ims(arr1*arr2)
raw_input("enter to continue")
pl.close('all')
