def compare_smoothmasks():
fname = input_mask_fname #"sdmf_pyxelmask.h5"
fid = h5py.File(fname, "r")
ds_orbits = fid["orbits"]
idx = np.argsort(ds_orbits[:])
orbits32 = ds_orbits[:][idx]
start_orbit = np.min(orbits32)
stop_orbit = np.max(orbits32)
orbit_range = stop_orbit - start_orbit + 1
#print(idx)
ds_combi = fid["combined"]
num_orbits = idx.size
a = np.empty((num_orbits,1024), dtype=np.float)
for i_orbit in range(num_orbits):
id_ = idx[i_orbit]
orbit = orbits32[i_orbit]
# a[orbit-start_orbit,:] = ds_combi[id_,:]
a[i_orbit,:] = ds_combi[id_,:]
fid.close()
print("3.2 read")
# print(a.shape)
# plt.cla()
# plt.imshow(a)
# plt.show()
# fname_out = "orbital_dbqm.h5"
# fid_out = h5py.File(fname_out, "w")
# ds = fid_out.create_dataset("data", a.shape, dtype=np.float)
# ds[:,:] = a
# fid_out.close()
# np.savetxt("orbital_dbqm.csv", a, delimiter=",")
m = Mask()
a_binary = np.ones((1400,1024), dtype=np.bool)
i_orbit = 0
orbits30 = np.empty(1400, dtype=np.int)
for orbit in range(42000,43400):
#print(orbit)
try:
m.load_sdmf30_crit(orbit, "combined", smooth=True)
except:
continue
orbits30[i_orbit] = orbit
a_binary[i_orbit,:] = m.mask
i_orbit += 1
print("3.0 read")
orbits30 = orbits30[0:i_orbit]
a_binary = a_binary[0:i_orbit,:]
for pixnr in range(1024):
plot(orbits32, a, orbits30, a_binary, pixnr)
return
def compare_combined_30_vs_32(orbit):
m30 = Mask()
m30.load_sdmf30_crit(orbit, "combined")
m32 = Mask()
m32.load_sdmf32_mask(orbit, "combinedFlag")
# for i in range(1024):
# print(i, m30.mask[i], m32.mask[i])
newdead = m30.get_new_dead(m32)
newalive = m30.get_new_alive(m32)
print("3.0->3.2 dead:", newdead.size, newdead)
print("3.0->3.2 alive:", newalive.size, newalive)
return
def compare_error_30_vs_32(orbit):
m30 = Mask()
m30_combined = Mask()
m30_combined.load_ascii(orbit)
m30.load_sdmf30_crit(orbit, "residual")
m32 = Mask()
m32.load_sdmf32_figure(orbit, "darkError")
newdead = m30.get_new_dead(m32)
newalive = m30.get_new_alive(m32)
print("new errory:", newdead.size, newdead)
print("new errory but already bad", np.sum(np.in1d(newdead, np.where(m30_combined.mask))))
print("real new errory", newdead[np.in1d(newdead, np.where(m30_combined.mask), invert=True)])
print("new not-errory:", newalive.size, newalive)
print("new not-errory but still bad", np.sum(np.in1d(newalive, np.where(m30_combined.mask))))
print("real new not-errory:", newalive[np.in1d(newalive, np.where(m30_combined.mask), invert=True)])
return
