# mean B
br_m = a.vals[:, :, a.lut[801], j].reshape((1, nt, nr))
bt_m = a.vals[:, :, a.lut[802], j].reshape((1, nt, nr))
bp_m = a.vals[:, :, a.lut[803], j].reshape((1, nt, nr))
# mean poloidal vort. and current (for phi-derivatives)
omr_m = a.vals[:, :, a.lut[301], j].reshape((1, nt, nr))
omt_m = a.vals[:, :, a.lut[302], j].reshape((1, nt, nr))
jr_m = a.vals[:, :, a.lut[1001], j].reshape((1, nt, nr))
if not bad_jt:
jt_m = a.vals[:, :, a.lut[1004], j].reshape((1, nt, nr))
else:
jt_m = np.zeros_like(jr_m)
# full (poloidal) derivatives v
dvrdr = drad(vr, rr)
dvtdr = drad(vt, rr)
dvpdr = drad(vp, rr)
dvrdt = dth(vr, tt) / rr_3d
dvtdt = dth(vt, tt) / rr_3d
dvpdt = dth(vp, tt) / rr_3d
# full (toroidal) derivatives v
dvrdp = dvpdr + (1. / rr_3d) * vp + omt
dvtdp = dvpdt + (cott_3d / rr_3d) * vp - omr
dvpdp = -dlnrho*vr - dvrdr - (2./rr_3d)*vr - dvtdt -\
(cott_3d/rr_3d)*vt
# full (poloidal) derivatives B
dbrdr = drad(br, rr)
# mean B
br_m = a.vals[:, :, a.lut[801], j].reshape((1, nt, nr))
bt_m = a.vals[:, :, a.lut[802], j].reshape((1, nt, nr))
bp_m = a.vals[:, :, a.lut[803], j].reshape((1, nt, nr))
# mean poloidal vort. and current (for phi-derivatives)
omr_m = a.vals[:, :, a.lut[301], j].reshape((1, nt, nr))
omt_m = a.vals[:, :, a.lut[302], j].reshape((1, nt, nr))
jr_m = a.vals[:, :, a.lut[1001], j].reshape((1, nt, nr))
if not bad_jt:
jt_m = a.vals[:, :, a.lut[1004], j].reshape((1, nt, nr))
else:
jt_m = np.zeros_like(jr_m)
# full (poloidal) derivatives v
dvrdr = drad(vr, rr)
dvtdr = drad(vt, rr)
dvpdr = drad(vp, rr)
dvrdt = dth(vr, tt) / rr_3d
dvtdt = dth(vt, tt) / rr_3d
dvpdt = dth(vp, tt) / rr_3d
# full (toroidal) derivatives v
dvrdp = dvpdr + (1. / rr_3d) * vp + omt
dvtdp = dvpdt + (cott_3d / rr_3d) * vp - omr
dvpdp = -dlnrho*vr - dvrdr - (2./rr_3d)*vr - dvtdt -\
(cott_3d/rr_3d)*vt
# full (poloidal) derivatives B
dbrdr = drad(br, rr)
bt_m = a.vals[:, :, a.lut[802], j].reshape((1, nt, nr))
# calculate flux terms
Bp_meanshear = (br*dOmdr + bt*dOmdt)
Bp_meanshear_m = (br_m*dOmdr + bt_m*dOmdt)
# still needs to be multiplied by a time scale...
# these are angular momentum fluxes from magnetic tension
# they include the factor of 4pi (in geofactor)
flux_r = np.mean(geofactor*Bp_meanshear*br, axis=0)*my_weight
flux_t = np.mean(geofactor*Bp_meanshear*bt, axis=0)*my_weight
flux_r_m = np.mean(geofactor*Bp_meanshear_m*br_m, axis=0)*my_weight
flux_t_m = np.mean(geofactor*Bp_meanshear_m*bt_m, axis=0)*my_weight
torque_r = drad(flux_r, rr) + (2/rr_2d)*flux_r
torque_t = (1/rr_2d)*(dth(flux_t, tt) + cott_2d*flux_t)
torque = torque_r + torque_t
torque_r_m = drad(flux_r_m, rr) + (2/rr_2d)*flux_r_m
torque_t_m = (1/rr_2d)*(dth(flux_t_m, tt) + cott_2d*flux_t_m)
torque_m = torque_r_m + torque_t_m
# add to my_vals array
# first the full terms
indstart = 0
my_vals[:, :, indstart + 0] += flux_r
my_vals[:, :, indstart + 1] += flux_t
my_vals[:, :, indstart + 2] += torque_r
my_vals[:, :, indstart + 3] += torque_t
my_vals[:, :, indstart + 4] += torque
for i in range(my_nfiles):
a = reading_func1(radatadir1 + str(my_files[i]).zfill(8), '')
mer = reading_func2(radatadir2 + str(my_files[i]).zfill(8), '')
niter = min(a.niter, mer.niter)
my_weight = 1.0 / (nfiles * niter)
for j in range(niter - 1, -1, -1): # go last to first in case "niters"
# don't agree
# mean fields
br_m = a.vals[:, :, a.lut[801], 0].reshape((1, nt, nr))
bt_m = a.vals[:, :, a.lut[802], 0].reshape((1, nt, nr))
bp_m = a.vals[:, :, a.lut[803], 0].reshape((1, nt, nr))
# mean derivs
dbrdr_m = drad(br_m, rr)
dbtdt_m = dth(bt_m, tt)
dbpdr_m = drad(bp_m, rr)
dbpdt_m = dth(bp_m, tt)
# full fields
br = mer.vals[:, :, :, mer.lut[801], 0]
bt = mer.vals[:, :, :, mer.lut[802], 0]
bp = mer.vals[:, :, :, mer.lut[803], 0]
# full derivs
dbrdr = drad(br, rr)
dbtdt = dth(bt, tt)
dbpdr = drad(bp, rr)
dbpdt = dth(bp, tt)