exp_data = R_exp
exp_err = dR_exp
if fit_normalized:
exp_data = R_exp_n
exp_err = dR_exp_n
# now read the data
views = []
for i, f in enumerate(view_files):
print(i)
print(f)
vv = vd.view(f, neutron_camera=False) # is_NC[i]) my mod Alex
views.append(vv)
xv = np.arange(len(views))
# get zero crossings
R0 = np.array([get_zero_crossing(v) for v in views])
# define the fitting function:
def S_f(x):
eff = []
for i in x:
global r_maxis, z_maxis
r_maxis, z_maxis = mag_axis[i]
v = views[i]
if v.is_NC:
view_dir+'track_h_neg_1.00.data', \
view_dir+'track_h_neg_2.00.data', \
view_dir+'track_h_pos_0.00.data', \
view_dir+'track_h_pos_1.00.data', \
view_dir+'track_v_neg_1.00.data', \
view_dir+'track_v_pos_0.00.data', \
view_dir+'track_v_neg_2.00.data', \
view_dir+'track_v_pos_1.00.data', \
]
data_sets[view_name] = view_files
# initialize data and setup exp. data file
view_files = data_sets[use_data_set]
Ntot = total_rate * integration_time
views = [vd.view(f) for f in view_files]
exp_dat = [view.eff_exp for view in views]
exp_counts = np.array(exp_dat) * Ntot
xv = np.arange(len(views))
exp_err = np.sqrt(exp_counts)
exp_err.clip(1., out=exp_err) * 1. # make sure the min.error is 1 not 0
sigma = 1. / exp_err
exp_eff = np.zeros_like(exp_counts)
for i, e in enumerate(exp_counts):
random_factor = 1. + np.random.normal(scale=sigma[i])
exp_eff[i] = max(e * random_factor, 0.)
#
# add an error
PD_views_f = []
PD_accept = []
PD_channel_number = []
PD_step = []
for i, i_d in enumerate(idet):
cc = ch[i]
idd = idet[i]
name_patt = orbit_output_dir + '/track_{}*.data'.format(i_d)
PD_view_files = G.glob(name_patt)
# use the first file in the list to get some parameters used for calculating rates
PDd = DF.pdfile(PD_view_files[0])
PD_accept.append(PDd.par.get_value('accept'))
PD_channel_number.append(PDd.par.get_value('channel_number', var_type=int))
PD_step.append(PDd.par.get_value('stepsize'))
# load the trajectories for each view
PD_v = [vd.view(f) for f in PD_view_files]
PD_views_f.append(PD_view_files)
PD_views.append(PD_v)
print('channel : ', cc, ', detecor : ', idd, ' loaded')
PD_accept = np.array(PD_accept)
#----------------------------------------------------------------------
# start drawing
#----------------------------------------------------------------------
# draw side view
if draw_top_view:
f1 = pl.figure(1, figsize=(11, 6))
else:
f1 = pl.figure(1, figsize=(5, 8))
f1.text(0.1, 0.925, eq_file)
def main(c_file):
control_file = c_file
# contpour plotting controle
cont_scale = 1.0
ncont = 25
#colormap = pl.get_cmap('CMRmap')
#colormap = pl.get_cmap('gnuplot')
#colormap = pl.get_cmap('gnuplot2')
colormap = pl.get_cmap('gist_heat')
#colormap = pl.get_cmap('jet')
#colormap = pl.get_cmap('hot')
# open control file
p_file = control_file
pf = PF.pfile(p_file)
output_dir = pf.get_value('output_dir')
# ???
plot_em = pf.get_value('plot_em', var_type=pf.Bool)
# ??
em_filled = pf.get_value('em_filled', var_type=pf.Bool)
# ??
plot_psirel = pf.get_value('plot_psirel', var_type=pf.Bool)
# ??
psirel_filled = pf.get_value('psirel_filled', var_type=pf.Bool)
##
all_yellow = pf.get_value('all_yellow', var_type=pf.Bool)
# color array
if all_yellow:
colors = ['y', 'y', 'y', 'y', 'y', 'y', 'y', 'y', 'y',
'y'] # make everything yellow
else:
colors = [
'red', 'green', 'blue', 'yellow', 'magenta', 'cyan', 'orange',
'lavenderblush', 'maroon', 'plum'
]
# dynamic input file for detector/channels assignment
di_dir = pf.get_value('dynamic_dir')
di_file = pf.get_value('dynamic_file')
dynamic_file = di_dir + di_file
df = B.get_file(dynamic_file)
dfd = df.par
# get channel/detector assignement
try:
dn = np.array(dfd.get_value('detector_to_use').split(','), dtype=int)
except:
dn = np.array([int(dfd.get_value('detector_to_use'))])
channel_number = B.get_data(df, 'ch')
detector_id = B.get_data(df, 'detector_id')
ch_touse = channel_number[np.in1d(detector_id, dn)]
# flux grid
try:
flux_data_file = pf.get_value('flux_data_file')
except:
flux_data_file = 'flux.data'
print('using : ', flux_data_file, ' for flux and Em data')
# flux limiter
try:
flux_limiter_file = pf.get_value('flux_limiter_file')
except:
flux_limiter_file = 'flux_limit.data'
print('using : ', flux_limiter_file, ' for flux limit data')
# plot n-flux at mid-plane
try:
flux_data_file_mp = pf.get_value('flux_data_file_mp')
except:
flux_data_file_mp = None
orbit_output = open(output_dir + 'orbit_output').readlines()
# find the EQ file used:
eq_file = 'generic'
for d in orbit_output:
if (d.find('--> EQ File unit, name') >= 0.):
eq_file = d.split()[-1:][0]
# flux
print('reading flux data')
fl = gf.flux(output_dir + flux_data_file)
print('reading flux limit data')
fll_d = DF.dfile(output_dir + flux_limiter_file)
r_fll = np.array(fll_d.get_data('xlim'))
z_fll = np.array(fll_d.get_data('ylim'))
# limiter
print('reading limiter data')
li = gl.limiter(output_dir + 'limiter_drawing.data')
#orbits
print('reading orbits data')
# each view can now have several trajectories
PD_views = []
PD_views_f = []
PD_accept = []
PD_channel_number = []
PD_step = []
for i, i_d in enumerate(dn):
cc = ch_touse[i]
name_patt = output_dir + '/track_{}*.data'.format(i_d)
PD_view_files = G.glob(name_patt)
# use the first file in the list to get some parameters used for calculating rates
PDd = DF.pdfile(PD_view_files[0])
PD_accept.append(PDd.par.get_value('accept'))
PD_channel_number.append(
PDd.par.get_value('channel_number', var_type=int))
PD_step.append(PDd.par.get_value('stepsize'))
# load the trajectories for each view
PD_v = [vd.view(f) for f in PD_view_files]
PD_views_f.append(PD_view_files)
PD_views.append(PD_v)
print('channel : ', cc, ', detecor : ', i_d, ' loaded')
PD_accept = np.array(PD_accept)
#----------------------------------------------------------------------
# start drawing
#----------------------------------------------------------------------
draw_top_view = pf.get_value('draw_top_view', var_type=pf.Bool)
draw_top_view = True
if draw_top_view:
f1 = pl.figure(figsize=(11, 6))
# f1=pickle.load(file('neutron.pickle_both_view'))
else:
f1 = pl.gcf() #figure(figsize= (5,8))
#f1.text(0.1, 0.925, eq_file)
# draw 3 regions
if draw_top_view:
li.draw_all()
else:
li.draw_side_all()
# draw the rel. flux
# get a nice set of contour lines
# select the first plot
Em_cont = None
# axes = li.ax1.get_axes()
# f1.sca( axes )
axes = li.ax1
f1.sca(axes)
# draw the flux limit
pl.plot(r_fll, z_fll, color='m', linewidth=2.)
if plot_em:
Em_range = fl.Em.max() - fl.Em.min()
Em_min = fl.Em.min() + Em_range / 100.
v = np.linspace(Em_min, fl.Em.max() * cont_scale, ncont)
if em_filled:
Em_cont = fl.draw_Em_filled(v, cmap=colormap)
else:
Em_cont = fl.draw_Em(v)
if plot_psirel:
v = np.linspace(-0.1, fl.Zrel.max(), 23)
if psirel_filled:
fl.draw_psirel_filled(v, cmap=colormap)
else:
fl.draw_psirel(v)
# make a nice x-axis
axes.xaxis.set_major_locator(MaxNLocator(4))
#----------------------------------------------------------------------
# draw orbits
#----------------------------------------------------------------------
# # animation part
# for i, PDv in enumerate(PD_views):
# icol = dn[i]-1
# animate(li, PDv, color = colors[icol])
#
# axes = li.ax1.get_axes()
axes = li.ax1
f1.sca(axes)
for i, PDv in enumerate(PD_views):
icol = dn[i] - 1
plot_view_side(PDv, color=colors[icol], label='Ch {0:d}'.format(dn[i]))
#pl.legend(fontsize = 12,loc = 'upper left')
pl.title
# draw orbits into the top view
#draw_top_view=True
if draw_top_view:
# axes = li.ax2.get_axes()
axes = li.ax2
f1.sca(axes)
# draw a few orbits
for i, PDv in enumerate(PD_views):
icol = dn[i] - 1
plot_view_top(PDv,
color=colors[icol],
label='Ch {0:d}'.format(dn[i]))
# get the mid-plane emmissivity
if plot_em:
if (flux_data_file_mp != None):
# load data file
d = np.load(output_dir + '/' + flux_data_file_mp)
X = d['X']
Y = d['Y']
Em_mid = d['EM_mp']
Em_range = Em_mid.max() - Em_mid.min()
Em_min = Em_mid.min() + Em_range / 100.
v = np.linspace(Em_min, Em_mid.max() * cont_scale, ncont)
if em_filled:
Em_cont = pl.contourf(X, Y, Em_mid, v, cmap=colormap)
else:
Em_cont = pl.contour(X, Y, Em_mid, v, cmap=colormap)
#pl.legend(fontsize = 12, loc = 'upper left')
# The following code plots histogram of orbits midplane intersection radii
# h_range =(0.0, 0.3)#(0.6, 1.6)
# r0 = []
# f2 = pl.figure()
# for i,PDv in enumerate( PD_views ):
# icol = dn[i]-1
# rr = []
# for v in PDv:
# rr.append(get_effective_dist(v))#get_zero_crossing(v))
#
# rr = np.array(rr)
# r = rr.mean()
# h = B.histo(rr, range =h_range, bins = 200)
# h.plot(color = colors[icol])
# print colors[icol]
# sig_r = np.sqrt(rr.var())
# r0.append([h, r, sig_r, rr])
# # all done
# pl.xlabel('R (m)')
# pl.ylabel('counts')
# pl.title('mid-plane Radius')
#
# #pl.savefig(orbit_dir+'image.png')
pl.show()
