fc = np.zeros(rms.size)
fc[cc] = (rms - rms_o)[cc]
b = B
#++++++++++++++++++++++++++++++++++++++++++++++++ figura
fig = figure(1, figsize=(6, 3.))
ax = fig.add_subplot(111)
#ax1 = ax.twinx()
#--- plot der
#ax1.plot(t[1:-1], fc[1:-1], c='gray') # rmsB
#ax1.plot(t[1:-1], b[1:-1], c='gray') # B field
tau, bp = 2.36, 0.0
q, off, bo = -9.373, 0.89, 16.15
ncr = ff.nCR2([t, fc, b], tau, q, off, bp, bo)
sqr = np.nanmean(np.power(crs - ncr, 2.0))
#--- plot izq
ax.plot(org_t, org_crs, '-o', c='gray', ms=3)
ax.plot(t, ncr, '-', c='red', lw=5, alpha=0.8, label='$\\tau=%3.3g$' % tau)
#++++ region sheath (naranja)
trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
rect1 = patches.Rectangle((0., 0.),
width=1,
height=1,
transform=trans,
color='orange',
alpha=0.3)
ax.add_patch(rect1)
fc = np.zeros(rms.size)
fc[cc] = (rms - rms_o)[cc]
#tau = 3.0
#ncr = nCR2([t, fc], tau, q)
#++++++++++++++++++++++++++++++++++++++++++++++++ figura
fig = figure(1, figsize=(6, 4))
ax0 = fig.add_subplot(111)
ax1 = ax0.twinx()
#--- plot der
#ax1.plot(t[1:-1], fc[1:-1], c='gray')
for tau in (4.0, 14.2, 7.14): #, 4.0):
#ncr = ff.func_nCR([t, fc], 0.0, tau, q)
ncr = ff.nCR2([t, fc], tau, q)
sqr = np.nanmean(np.power(crs - ncr, 2.0))
print sqr
#--- plot izq
ax0.plot(t, ncr, lw=3, label='$\\tau=%3.3g$' % tau)
ax0.plot(t, crs, '-o', c='k', ms=3)
ax0.axhline(y=0.0, c='g')
ax0.axvline(x=0, ls='--', c='gray', lw=3)
ax0.axvline(x=1, ls='--', c='gray', lw=3)
ax0.axvline(x=4, ls='--', c='gray', lw=3)
ax0.legend()
ax0.grid()
ax0.set_ylabel('n_CR [%]')
ax0.set_xlim(-2, +7)
ax0.set_ylim(-4, +2.)
fo = h5(fname_out, 'w')
for pname in fit.par.keys():
fo[pname] = fit.par[pname]
#--- guardamos la grilla de exploracion
fo['grids/tau'] = [tau.min, tau.max, tau.delta(), tau.n]
fo['grids/q'] = [q.min, q.max, q.delta(), q.n]
fo['grids/off'] = [off.min, off.max, off.delta(), off.n]
fo['grids/bp'] = [bp.min, bp.max, bp.delta(), bp.n]
fo['grids/bo'] = [bo.min, bo.max, bo.delta(), bo.n]
#------------------
#++++++++++++++++++++++++++++++++++++++++++++++++ figura
fig = figure(1, figsize=(6, 3.))
ax = fig.add_subplot(111)
ncr = ff.nCR2([t, fc, b], **fit.par)
sqr = np.nanmean(np.square(crs - ncr))
#--- plot izq
ax.plot(org_t, org_crs, '-o', c='gray', ms=3)
ax.plot(t,
ncr,
'-',
c='red',
lw=5,
alpha=0.8,
label='$\\{tau:3.3g}$'.format(**fit.par))
#++++ region sheath (naranja)
trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
rect1 = patches.Rectangle((0., 0.),
slice(off.min, off.max, off.delta()),
slice(bp.min, bp.max, bp.delta()),
slice(bo.min, bo.max, bo.delta()),
)
#--- start && run the fitter
data = np.array([t, fc, crs, b], dtype=np.float32)
sems = np.array([tau_, q_, off_, bp_, bo_], dtype=np.int)
fit = cf.fit_forbush(data, sems)
par = fit.make_fit_brute(rranges)
print par # resultado
#++++++++++++++++++++++++++++++++++++++++++++++++ figura
fig = figure(1, figsize=(6, 3.))
ax = fig.add_subplot(111)
ncr = ff.nCR2([t, fc, b], **par)
sqr = np.nanmean(np.square(crs - ncr))
#--- plot izq
ax.plot(org_t, org_crs, '-o', c='gray', ms=3)
ax.plot(t,
ncr,
'-',
c='red',
lw=5,
alpha=0.8,
label='$\\{tau:3.3g}$'.format(**par))
#++++ region sheath (naranja)
trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
rect1 = patches.Rectangle((0., 0.),