def test_radial_profiles():
arr = random_periodic_upsample(128, 16, seed=0)
mask = np.zeros(arr.shape, dtype=np.bool_)
arr_x = vcalc.cderivative(arr, 'X_DIR')
arr_y = vcalc.cderivative(arr, 'Y_DIR')
arr_div = np.sqrt(arr_x**2 + arr_y**2)
surf = _cp.TopoSurface(arr)
rprofs = radial_profiles(surf, threshold=25, expand_regions=1, other_arr=arr_div, mask=mask)
arr[mask] = 2 * arr.max()
pl.imshow(arr, interpolation='nearest')
pl.figure()
pl.imshow(arr_div)
pl.figure()
pl.hold(True)
linreg_xy = ([], [])
for minmax, (rprof, region) in rprofs.items():
# minmax_flux = arr_div[minmax]
pts, fluxes, avg_fluxes, avg_fluxes_errs, avg_dists, avg_dists_errs = \
zip(*rprof)
linreg_xy[0].extend(fluxes)
linreg_xy[1].extend(avg_fluxes)
# fluxes = np.abs(np.array(fluxes) - minmax_flux)
# avg_fluxes = np.abs(np.array(avg_fluxes) - minmax_flux)
# pl.plot(avg_dists, avg_fluxes, 'd-')
pl.plot(avg_dists, avg_fluxes, 'd-')
pl.grid()
slope, intercept, rval, pval, stderr = stats.linregress(*linreg_xy)
print
print "slope: %f" % slope
print "intercept: %f" % intercept
print "rval: %f" % rval
print "pval: %f" % pval
print "stderr: %f" % stderr
import pdb; pdb.set_trace()
def mag_shear_rad_profs(psi_arr):
psi_x, psi_y = gradient(psi_arr)
bmag = np.sqrt(psi_x**2 + psi_y**2)
psi_arr = gaussian_filter(psi_arr, sigma=2.0, mode='wrap')
mshear = mag_shear(psi_arr)
surf = _cp.TopoSurface(psi_arr)
regions = surf.get_minmax_regions()
for (minmax, pss, type), region in regions:
pass
rprofs = radial_profiles(surf, threshold=25, other_arr=bmag)
pl.ion()
pl.figure()
for minmax, (rprof, region) in rprofs.items():
# minmax_flux = arr_div[minmax]
pts, extremum_val, avg_bmags, avg_bmags_errs, avg_dists, avg_dists_errs = \
zip(*rprof)
# fluxes = np.abs(np.array(fluxes) - minmax_flux)
# avg_fluxes = np.abs(np.array(avg_fluxes) - minmax_flux)
pl.subplot(211)
pl.plot(avg_dists, avg_bmags, 'd-')
pl.subplot(212)
pl.imshow(bmag)
# pl.plot(avg_dists, avg_shear, 'd-')
raw_input('enter to continue')
pl.clf()
import pdb; pdb.set_trace()
pl.close('all')
def test_radial_profiles():
psi_arr = nth_timeslice('data.h5', 'psi', -1)
bx_arr = nth_timeslice('data.h5', 'bx', -1)
by_arr = nth_timeslice('data.h5', 'by', -1)
# for bx_arr, by_arr, psi_arr in h5_gen('data.h5', ('bx', 'by', 'psi')):
psi_arr = psi_arr.astype(np.double)
mask = np.zeros(psi_arr.shape, dtype=np.bool_)
bmag = np.sqrt(bx_arr**2 + by_arr**2).astype(np.double)
surf = _cp.TopoSurface(psi_arr)
rprofs = radial_profiles(surf, threshold=50, expand_regions=10,
other_arr=bmag, mask=mask)
# psi_arr[mask] = 2 * psi_arr.max()
# pl.figure()
# pl.imshow(psi_arr, interpolation='nearest')
# pl.figure()
# pl.imshow(bmag)
bmag_fig = pl.figure()
fluxes_vs_dists = pl.figure()
fluxes_vs_flux = pl.figure()
pl.hold(False)
for minmax, (rprof, region) in rprofs.items():
# minmax_flux = arr_div[minmax]
rprof.sort(key=lambda x: x[1])
import pdb; pdb.set_trace()
pts, fluxes, avg_b, avg_b_errs, avg_dists, avg_dists_errs = zip(*rprof)
region_xs, region_ys = zip(*region)
mask = np.ones(bmag.shape, dtype=np.bool_)
mask[region_xs, region_ys] = False
masked_bmag = bmag.copy()
masked_bmag[mask] = 0.0
pl.figure(bmag_fig.number)
pl.imshow(masked_bmag)
avg_b = np.array(avg_b)
avg_b -= avg_b[0]
avg_dists = np.array(avg_dists)
fluxes = np.array(fluxes)
fluxes -= fluxes.min()
# fluxes = np.abs(np.array(fluxes) - minmax_flux)
# avg_fluxes = np.abs(np.array(avg_fluxes) - minmax_flux)
# pl.plot(avg_dists, avg_fluxes, 'd-')
b_over_flux = (avg_b-(avg_b.min())) / fluxes
pl.figure(fluxes_vs_dists.number)
pl.plot(fluxes, avg_b, 'd-')
# pl.errorbar(avg_dists, b_over_flux, yerr=avg_fluxes_errs,
# hold=True)
pl.figure(fluxes_vs_flux.number)
# delta_r = np.diff(avg_dists)
delta_flux = np.diff(fluxes)
# dr_b_over_r = np.diff(b_over_r) / delta_r
dr_b_over_flux = np.diff(b_over_flux) / delta_flux
# pl.plot(avg_dists, (avg_fluxes-avg_fluxes.min()) / avg_dists, 'x-')
pl.plot(fluxes[:-1], dr_b_over_flux, 'x-')
# pl.errorbar(avg_dists[:-1], dr_b_over_r,
# xerr=avg_dists_errs[:-1],
# yerr=avg_fluxes_errs[:-1],
# hold=True)
raw_input("enter to continue")
pl.figure(fluxes_vs_dists.number)
pl.grid()
pl.figure(fluxes_vs_flux.number)
pl.grid()
import pdb; pdb.set_trace()
pl.close('all')
