def dump_SF2(Rmag, SF2, outname):
dx = 1. / 128
bins = np.arange(0.5 * dx, 2, dx)
binner = rb.rb2(Rmag / 128, SF2, bins=bins)
ax.plot(binner[1], binner[2])
dpy_save(outname, {
'R': binner[1],
'SF2L': binner[2]
},
fields=['R', 'SF2L'])
fig.savefig('plots_to_sort/thing2.png')
plt.close(fig)
def binnedplot(fig, ax, Rmag, SF2):
dx = 1. / 128
bins = np.arange(0.5 * dx, 2, dx)
#reload(rb)
binner = rb.rb2(Rmag / 128, SF2, bins=bins)
rbins = binner[1]
SF2bins = binner[2]
ax.plot(rbins, SF2bins)
ok = np.logical_and(rbins > 0, SF2bins > 0)
ok = np.logical_and(ok, np.isnan(SF2bins) == False)
ok = np.logical_and(ok, rbins < 0.5)
if 0:
X, Y = binner[1][ok], binner[2][ok]
pfit = np.polyfit(np.log10(X), np.log10(Y), 1)
logx = np.log10(X)
ax.plot(X, 10**(logx * pfit[0] + pfit[1]), c='k')
if 1:
X, Y = binner[1][ok], binner[2][ok]
pfit = np.polyfit(X, Y, 1)
logx = np.log10(X)
ax.plot(X, (X * pfit[0] + pfit[1]), c='k')
SF2L = [[], [], []]
Things = []
R[0], SF2L[0] = dpy(dl.sf_path + '/SF2_L_subset_2_u05.h5',
['Rmag', 'SF2_L'])
R[1], SF2L[1] = dpy(dl.sf_path + '/SF2_L_subset_2_u10.h5',
['Rmag', 'SF2_L'])
R[2], SF2L[2] = dpy(dl.sf_path + '/SF2_L_subset_2_u11.h5',
['Rmag', 'SF2_L'])
SCALE = 128**6
SCALE = 1 / 128**3
import radial_binner as rb
reload(rb)
for n in range(3):
#bins = np.linspace(0,128,129)/128
bins = np.linspace(0, 64, 65) / 64
bin_edge, bin_center, values = rb.rb2(R[n] / 128, SF2L[n], bins=bins)
bin_center, values = clean(bin_center, values)
Things.append([bin_center, values])
SCALE = 128**6
SCALE = 1 / 128**3
if 1:
import sf2
reload(sf2)
for nrun, this_run in enumerate([run1, run2, run3]):
if 'msf' not in dir() or True:
msf = sf2.make_sf(this_run.this_looper, 0)
rbins, SS = msf.bin_sf2()
SS /= 2 * np.pi
#plot(this_run,0, my_sf2=[rbins,SS])
im1=rho.sum(axis=1)
im2=AC.sum(axis=1)
ax1.imshow(im1)
ax2.imshow(im2)
#ax1.plot(rmag[33,33:], AC[33,33:],c='k')
ACstripe = AC[33,33:]
ax3.plot( ACstripe,c='k',label='slice')
r1 = np.arange(31)/32 #rmag[33,33:]
y = 2.0*(np.arccos(r1)- r1*np.sqrt(1.0-r1**2))/np.pi;
#y = radius*(radius*np.arccos(r1/radius)- r1*np.sqrt(1.0-r1**2/radius**2))/np.pi/128
ax3.plot( y,c='g',label='ann')
import radial_binner as rb
ACbins,ACcen,ACvals = rb.rb2(rmag, AC,nbins=33)
ax3.plot(ACcen,ACvals,c='r', label='binned')
dr = ACbins[1:]-ACbins[:-1]
L = np.sum(ACvals*dr)/ACvals[0]
rect=patches.Rectangle((0,0),L,ACvals[0],facecolor=[0.2]*3)
ax3.add_patch(rect)
print("L = %0.2f R = %0.2f"%(L,radius))
bins,cen,vals = rb.rb2(rmag, rho/rho.max())
ax3.plot(cen,vals,c='m')
ax3.legend(loc=0)
fig.savefig('plots_to_sort/AC1.png')
if 0: