def field_with_streams(F, Vx, Vy, outname):
fig, ax = plt.subplots()
size = F.shape
dx = 1. / nar(size)
Y, X = np.mgrid[0:size[1]:1, 0:size[0]:1]
ax.imshow(F, origin='lower', interpolation='nearest')
Vxbar = np.mean(Vx)
varx = Vx - Vxbar
Vybar = np.mean(Vy)
vary = Vy - Vybar
stat(varx)
ax.streamplot(X, Y, 10 * varx + 0.1 * Vxbar, 10 * vary + 0.1 * Vybar)
fig.savefig(outname)
plt.close(fig)
turb_quan.plotter2(
[Vx - np.mean(Vx), Vy - np.mean(Vy)],
'p49c_plots/means.png',
norm='ind',
#axis_labels=stuff['axis_labels'],
labs=['x-xbar', 'y-ybar'])
def wave_to_EB(wave, theta=np.pi / 4, size=16, mode_tool=modes_k001):
state, fft = modes(theta=theta,
wave=wave,
h0=1,
size=size,
mode_tool=mode_tool)
qu = state_to_qu(state, line_of_sight=0)
#E,B,Eh,Bh,Qh,Uh = p49_QU2EB.EBfromQU(qu['Q'],qu['U'], return_quharm=True)
EB = p49_QU2EB.EBfromQU(qu['Q'], qu['U'], return_quharm=True)
turb_quan.plotter(qu['Q'],
qu['U'],
EB['E'],
EB['B'],
'p49c_plots/test_EB_%s_%02f.png' % (wave, theta),
norm='ind',
axis_labels=qu['axis_labels'])
flat = {}
for f in ['n', 'H2', 'Hh', 'Hv']:
flat[f] = np.sum(qu[f], axis=0)
flat[f].shape = (qu[f].shape[0], qu[f].shape[1])
turb_quan.plotter2([flat['n'], flat['H2'], flat['Hh'], flat['Hv']],
'p49c_plots/test_Physics_%s_%0.2f.png' % (wave, theta),
labs=['n', 'B', 'Hh', 'Hv'],
norm='ind',
axis_labels=qu['axis_labels'])
plots.field_with_streams(flat['n'], flat['Hh'], flat['Hv'],
'p49c_plots/with_streams.png')
outname = 'p49c_plots/test_QhUhEhBh_%s_%0.2f.png' % (wave, theta)
turb_quan.plotter(np.abs(EB['Qh']),
np.abs(EB['Uh']),
np.abs(EB['Eh']),
np.abs(EB['Bh']),
outname,
labs=['Qh', 'Uh', 'Eh', 'Bh'],
norm='positive',
axis_labels=qu['axis_labels'])
print("Plot %s" % outname)
output = {'fft': fft, 's': state} #,'Q':qu['Q'],'U':qu['U'],'qu':qu}
output.update(qu)
output.update(EB)
return output
end = 1
y, x = np.mgrid[0:end:dx, 0:end:dx]
kp_unit = nar([0, 1]) * 2 * np.pi
rho = (np.exp(1j * (kp_unit[0] * x + kp_unit[1] * y))).imag
if 0:
kp_unit = nar([2, 1]) * 2 * np.pi
rho += (np.exp(1j * (kp_unit[0] * x + kp_unit[1] * y))).imag
rhohat = np.fft.rfftn(rho)
plt.imshow(rhohat.real)
plt.imshow(rhohat.imag)
import turb_quan
reload(turb_quan)
turb_quan.plotter2([rho, rho, rho],
labs=['rho', 'rhohat', 'rhohat i', 'rhohat real'],
norm='ind',
fname='p49c_plots/2dgames_xspace.png',
share=False)
turb_quan.plotter2([np.abs(rhohat)],
npx=1,
zmin=1e-11,
labs=['rhohat', 'rhohat i', 'rhohat real'],
norm='positive',
fname='p49c_plots/2dgames_kspace.png',
share=True)
if 0:
def su(x):
y = np.abs(x).sum()
if y < 1e-8:
return "--"
def stuff_3d(A=[], B=[], K=[], Theta=[], linthreshy=1e-8):
"""in 3d!"""
#x = np.arange(Q.size)
output = {}
size = 32
twopi = np.pi * 2
x, y, z = np.mgrid[0:1:1. / size, 0:1:1. / size, 0:1:1. / size]
nmodes = len(A)
consts = np.zeros([nmodes])
linears = np.zeros([nmodes, size, size, size])
sums = np.zeros([nmodes, size, size, size])
for n in range(nmodes):
consts[n] = A[n]
linears[n, ...] = B[n] * (np.exp(
1j * (2 * np.pi *
(K[n][0] * x + K[n][1] * y + K[n][2] * z) + Theta[n])))
sums[n, ...] = consts[n] + linears[n, ...]
plt.clf()
slc = slice(3, 4), slice(3, 4), slice(None)
the_x = z
#slc = slice(None), slice(3,4),slice(3,4);the_x=x
#slc = slice(None), slice(3,4),slice(3,4);the_x=x
#slc = slice(None);the_x=x
def xx(arr):
return arr[slc].flatten()
for n in range(nmodes):
plt.plot(xx(the_x), xx(sums[n, ...]), label=n)
Q_flat = np.prod(sums.real, axis=0)
plt.plot(xx(the_x), xx(Q_flat), label='A*B*C')
plt.legend(loc=0)
plt.savefig('p49c_plots/fft_3db_test.png')
actual_fft = np.fft.rfftn(Q_flat)
qfft = actual_fft / (Q_flat.size)
nonzero_k = nz(qfft)
nonzero_v = nonzero(qfft)
abs_qfft = np.abs(qfft)
zmin = abs_qfft[abs_qfft > 0].min()
turb_quan.plotter2(
[
lmax(qfft.real, 0),
lmax(qfft.imag, 0),
lmax(qfft.real, 1),
lmax(qfft.imag, 1),
lmax(qfft.real, 2),
lmax(qfft.imag, 2)
],
"p49c_plots/qfft_3da.png",
norm='positive',
zmin=zmin,
labs=['real x', 'imag x', 'real y', 'imag y', 'real z', 'imag z'],
share=False)
def plotter3(arr, axis):
dims = list(arr.shape)
dims.pop(axis)
flat = np.sum(arr, axis=axis)
flat.shape = dims
return flat
turb_quan.plotter2([
plotter3(sums[0, ...], 0),
plotter3(sums[1, ...], 0),
plotter3(sums[0, ...], 0),
plotter3(Q_flat, 0)
],
"p49c_plots/real_x_3da.png",
labs=["Ax", "Bx", "Cx", "ABCx"],
share=False,
norm='ind')
turb_quan.plotter2([
plotter3(sums[0, ...], 1),
plotter3(sums[1, ...], 1),
plotter3(sums[0, ...], 1),
plotter3(Q_flat, 1)
],
"p49c_plots/real_y_3da.png",
labs=["Ay", "By", "Cy", "ABCy"],
share=False,
norm='ind')
turb_quan.plotter2([
plotter3(sums[0, ...], 2),
plotter3(sums[1, ...], 2),
plotter3(sums[0, ...], 2),
plotter3(Q_flat, 2)
],
"p49c_plots/real_z_3da.png",
labs=["Az", "Bz", "Cz", "ABCz"],
share=False,
norm='ind')
values = {}
values['A'] = nar(A)
values['B'] = nar(B)
values['K'] = nar(K)
values['Theta'] = nar(Theta)
reload(amp_tools)
expect = amp_tools.modes_2(**values)
output['labs'] = expect.pop('labs')
output['full'] = expect.pop('full_history')
output.update({'Q_flat': Q_flat, 'acutal_fft': actual_fft, 'qfft': qfft})
output.update(values)
output['expect'] = expect
output['nonzero_v'] = nonzero_v
output['nonzero_k'] = zip(*nonzero_k)
output['nonz'] = dict(zip(output['nonzero_k'], output['nonzero_v']))
def sanitize_real_vectors(ft):
for v in ft:
if v[2] == 0:
pass
#sanitize_real_vectors( output['nonz'])
total_error = 0
for n, vec in enumerate(output['nonzero_k']):
if vec in expect:
this_error = np.abs(expect[vec] - nonzero_v[n])
total_error += this_error
if this_error > 1e-12:
print("expect off. actual %0.2e expect %0.2e diff %0.2d vec %s"%\
( nonzero_v[n], expect[vec], this_error, str(vec)))
else:
print("expect missing vector %s" % str(vec))
print("Total error: %0.2e" % total_error)
#expect_k = nar(sorted(list(expect.keys())))
#expect_v = nar([ expect[ key] for key in expect_k])
#output['expect_v']=expect_v
#output['expect_k']=expect_k
output['total_error'] = total_error
return output
def stuff_3d(save=None):
"""in 3d!"""
#x = np.arange(Q.size)
output={}
size=32
twopi=np.pi*2
x,y,z = np.mgrid[0:1:1./size,0:1:1./size,0:1:1./size]
#x = np.mgrid[0:1:1./size]#,0:1:1./size,0:1:1./size]
#y=z=1
#y=0
#z=0
#k_unit_int = nar([1,7,1])
#k_unit_Q = np.array(k_unit_int)*twopi
#ampl=0.1
#Q_flat = ampl* (np.exp(1j*(k_unit_Q[0]*x+k_unit_Q[1]*y+k_unit_Q[2]*z))).real
A0 = 1
A1 = np.sqrt(2.e-6)
KAx=0
KAy=0
KAz=1
KA = nar([KAx,KAy,KAz])
Aa = A0
Atheta = 0
Ab = A1* (np.exp(1j*(2*np.pi*(KAx*x+KAy*y+KAz*z)+Atheta))).real
A = Aa+Ab
B0 = 1
B1 = np.sqrt(2.e-6)
KBx=0
KBy=0
KBz=1
KB = nar([KBx,KBy,KBz])
Ba = B0
Btheta = 0 ;-np.pi/2.
Bb = B1* (np.exp(1j*(2*np.pi*(KBx*x+KBy*y+KBz*z)+Btheta))).real
B = Ba+Bb
output['B']=B
C0 = 1
C1 = 0
KCx=5
KCy=0
KCz=0
KC = nar([KCx,KCy,KCz])
Ca = C0
Ctheta=1.2
Cb = C1* (np.exp(1j*(2*np.pi*(KCx*x+KCy*y+KCz*z)+Ctheta))).real
C = Ca+Cb
plt.clf()
slc = slice(3,4),slice(3,4),slice(None);the_x = z
#slc = slice(None), slice(3,4),slice(3,4);the_x=x
#slc = slice(None), slice(3,4),slice(3,4);the_x=x
#slc = slice(None);the_x=x
def xx(arr):
return arr[slc].flatten()
plt.plot(xx(the_x),xx(A),label='A')
plt.plot(xx(the_x),xx(B),label='B')
plt.plot(xx(the_x),xx(C),label='C')
plt.plot(xx(the_x),xx(A*B*C),label='A*B*C')
plt.legend(loc=0)
plt.savefig('p49c_plots/fft7_test.png')
if save is None:
Q_flat = (A*B*C)
actual_fft=np.fft.rfftn(Q_flat)
else:
Q_flat = (A*B*C)[slc]
Q_flat = save['Q_flat']
Q_flat.shape = Q_flat.size
actual_fft=np.fft.rfft(Q_flat)
qfft=actual_fft/(Q_flat.size)
nonzero_k= nz(qfft)
nonzero_v = nonzero(qfft)
turb_quan.plotter2( [lmax( qfft.real, 0),
lmax( qfft.imag, 0),
lmax( qfft.real, 1),
lmax( qfft.imag, 1),
lmax( qfft.real, 2),
lmax( qfft.imag, 2)],
"p49c_plots/qfft_3da.png",
labs=['real x','imag x','real y','imag y','real z','imag z'],
share=False)
def plotter3(arr,axis):
dims = list(arr.shape)
dims.pop(axis)
flat = np.sum(arr,axis=axis)
flat.shape=dims
return flat
turb_quan.plotter2( [plotter3( A, 0),
plotter3( B, 0),
plotter3( C, 0),
plotter3( A*B*C, 0)],
"p49c_plots/real_x_3da.png",
labs=["Ax","Bx","Cx","ABCx"],
share=False,norm='ind')
turb_quan.plotter2( [plotter3( A, 1),
plotter3( B, 1),
plotter3( C, 1),
plotter3( A*B*C, 1)],
"p49c_plots/real_y_3da.png",
labs=["Ay","By","Cy","ABCy"],
share=False,norm='ind')
turb_quan.plotter2( [plotter3( A, 2),
plotter3( B, 2),
plotter3( C, 2),
plotter3( A*B*C, 2)],
"p49c_plots/real_z_3da.png",
labs=["Az","Bz","Cz","ABCz"],
share=False,norm='ind')
values={}
values['KA']=KA[2]
values['KB']=KB[2]
values['KC']=KC[2]
values['A0']=A0
values['A1']=A1
values['B0']=B0
values['B1']=B1
values['C0']=C0
values['C1']=C1
values['Atheta']=Atheta
values['Btheta']=Btheta
values['Ctheta']=Ctheta
reload(amp_tools)
expect = amp_tools.modes_1(**values)
labs = expect.pop('labs')
output.update({'Q_flat':Q_flat,'acutal_fft':actual_fft,'qfft':qfft})
output.update(values)
expect_k = nar(sorted(list(expect.keys())))
expect_v = nar([ expect[ key] for key in expect_k])
output['expect']=expect
output['nonzero_v']=nonzero_v
output['nonzero_k']=nonzero_k
output['expect_v']=expect_v
output['expect_k']=expect_k
return output
if not os.path.exists(fname):
print("shoot. Missing %s"%fname)
continue
#if not os.path.exists(field):
# print("shoot. Missing %s"%field)
d[car.name]=di=pyfits.open(density,dtype=np.double)[0].data
h[car.name]=hi=pyfits.open(field,dtype=np.double)[0].data
q[car.name]=qi=pyfits.open(Q,dtype=np.double)[0].data
u[car.name]=ui=pyfits.open(U,dtype=np.double)[0].data
e[car.name]=ei=pyfits.open(E,dtype=np.double)[0].data
b[car.name]=bi=pyfits.open(B,dtype=np.double)[0].data
if 1:
outname = "p49six_%s_%s_%s.png"%(car.outname,set_output,ax)
turb_quan.plotter2([d[car.name], h[car.name],q[car.name],
u[car.name],e[car.name],b[car.name]],
outname,
labs=['d','H','q','u','e','b'],
norm='ind')
if 0:
outname = 'p49_qhist_%s_%s.png'%(car.outname,frame,set_output)
plt.hist(np.log(np.abs(q.flatten())),histtype='step',color='rgb'[nax])
if 1:
outname_base = "%s__%04d_frb_%s_%s.png"
for name, array in [ ['Q',qi],['U',ui],['E',ei],['B',bi]]:
outname = outname_base%( car.outname, frame, name, ax)
turb_quan.plotter2([array], outname, labs=[name], norm='ind',npx=1)
if 0:
outname = "p49six_density_%s_%s_%s.png"%(SERIES,set_output,ax)
turb_quan.plotter2( [d[name] for name in car_list],