def plot_data_set(self, rs='all', meass='all', lim=20, window=1.0,
norm=False):
#~ mpl.rcdefaults()
os.chdir('/home/jaime/Documents/ResearchTopics/Publications/Current Reflections/Data Sets/')
for rss in self.root.iter('return_stroke'):
r_s = int(rss.find('number').text)
if rs == -1:
pass
elif 'all' in rs.lower():
pass
elif r_s == int(rs):
pass
else:
continue
fileSave = "./DataFiles/%s_data_%s_rs%d.p" % (self.eventNamef,
self.eventDate,
r_s)
data = pickle.load(open(fileSave, "rb"))
data_only = {}
if "all" in meass:
ignore = []
E = ['E_5', 'E_18', 'E_23', 'E_12F', 'E_12', 'E_NW60',
'E_NW100']
dE = ['dE_1', 'dE_3', 'dE_4', 'dE_5', 'dE_7', 'dE_8', 'dE_9', 'dE_11', 'dE_17', 'dE_25', 'dE_OB']
intt = ['intE_1', 'intE_3', 'intE_4', 'intE_5', 'intE_7', 'intE_8', 'intE_9', 'intE_11', 'intE_17', 'intE_25', 'intE_OB']
ignore.extend(intt)
ignore.extend(E)
ignore.extend(['II_HI'])
# ignore.extend(dE)
for key in data:
if key in ignore:
pass
else:
data_only[key] = data[key]
keys = data_only.keys()
sort_data = {}
for i, key in enumerate(keys):
value = data[key].ax.get_title().split()
if len(value) == 4:
s = value[3]
ind = s.find('km')
if ind > 0:
temp = float(value[3][:ind])
s = '%d' % (temp*1E3)
ind = s.find('m')
if ind > 0:
s = value[3][:ind]
sort_data[key] = float(s)
else:
s = value[4]
ind = s.find('km')
if ind > 0:
temp = float(value[4][:ind])
s = '%d' % (temp*1E3)
ind = s.find('m')
if ind > 0:
s = value[4][:ind]
sort_data[key] = float(s)
sorted_data = sorted(sort_data.items(),
key=operator.itemgetter(1))
"""
Print all in individual figures
"""
mpl.rcParams['savefig.directory'] = '/home/jaime/Documents/My Papers/Ongoing/Current Reflections/LaTex/Pictures/Temp/'
for i in range(len(sorted_data)):
fig, ax = plt.subplots(1, 1)
#~ fig.suptitle('%s (%s/%s/%s)' % (event, eventDate[:2],eventDate[2:4], eventDate[4:]), fontsize=16)
#~ fig.suptitle(event)#, fontsize=16)
temp_data = data[sorted_data[i][0]].data
# print(sorted_data[i], len(data[sorted_data[i][0]]), len(temp_data), len(data['time']), np.max(temp_data))
zoom_lim = [-1, int(lim)]
# print(zoom_lim)
factor = 1E-3
l_width = 2.5
if 'dE_' in sorted_data[i][0]:
factor = 1E-9
l_width = 1.5
ax.plot(data[sorted_data[i][0]].dataTime, factor*temp_data,
linewidth=l_width)
ax.set_title(data[sorted_data[i][0]].ax.get_title())
if "ii_hi" in sorted_data[i][0].lower():
ylabel = '(kA)'
elif "int" in sorted_data[i][0].lower():
ylabel = '(kV/m)'
elif "de" in sorted_data[i][0].lower():
ylabel = '(kV/m/$\mu$s)'
elif "e_" in sorted_data[i][0].lower():
ylabel = '(kV/m)'
else:
ylabel = data[sorted_data[i][0]].ax.get_ylabel()
if '/us' in ylabel:
ylabel = ylabel[:-3] + ylabel[-1]
#~ ax.set_ylabel(ylabel)
ax.set_xlim(zoom_lim)
ax.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(True, which='both')
ax.xaxis.set_tick_params(width=1.5)
#~ ax.autoscale(True,'y')
#~ ax.set_xlabel('Time ($\mu$s)')
p = df.pickerPlot(fig, ax)
p.plot()
else:
mpl.rcParams['savefig.directory'] = '/home/jaime/Documents/My Papers/Ongoing/Current Reflections/LaTex/Pictures/Temp/'
x = data[meass].dataTime
y = data[meass].data
y_filtered = filters.moving_average(y, window)
#~ plt.plot(x, y, '-', x,y_filtered, '--')
norm_factor = 1.0
factor = 1E-3
l_width = 2.5
if 'dE_' in meass:
factor = 1E-9
l_width = 1.5
### Normalize waveform
if norm:
p = df.RelativeTimePlot(x,y)
p.plot()
plt.show()
zero = y[p.zero_ind]
p = df.RelativeTimePlot(x, y)
p.plot()
plt.show()
peak = y[p.zero_ind]
norm_factor = float(peak - zero)*factor
fig = plt.figure()
ax = fig.add_subplot(111)
zoom_lim = [-1, int(lim)]
title = '%s (RS #%d)' % (self.eventName, r_s)
# title = '%s (ICC)' % self.eventName
# title = data[meass].ax.get_title()
# if factor == 1E-3:
# ylabel = '(k%s' % (data[meass].ax.get_ylabel()[1:])
# else:
# ylabel = data[meass].ax.get_ylabel()
xlabel = 'Time ($\mu$s)'
ax.plot(x*1e6, y_filtered*factor/norm_factor, linewidth=l_width)
ax.set_title(title)
#~ ax.set_ylabel(ylabel)
#~ ax.set_xlabel(xlabel)
ax.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(True, which='both')
ax.xaxis.set_tick_params(width=1.5)
#~ ax.autoscale(True,'y')
ax.set_xlim(zoom_lim)
#~ ax.yaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
p = df.pickerPlot(fig, ax)
p.plot()
plt.show()
def plot_data(self):
print("Plotting results...")
mpl.rcdefaults()
mpl.rcParams['keymap.back'] = 'b'
mpl.rcParams['keymap.forward'] = 'b'
time = self.time - self.D/c
xlims = [-2, 80]
### All fields in one figure
fig = plt.figure()
fig.suptitle('Distance from channel: %d m' % self.D)
ax_es = fig.add_subplot(411)
ax_es.plot(time*1E6, self.E_es)
ax_es.set_ylabel('E_es (V/m)')
ax_es.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax_es.grid(True,which='both')
ax_es.set_xlim(xlims)
ax_es.autoscale(enable=True, axis='y', tight=True)
ax_ind = fig.add_subplot(412)
ax_ind.plot(time*1E6, self.E_ind)
ax_ind.set_ylabel('E_ind (V/m)')
ax_ind.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax_ind.grid(True,which='both')
ax_ind.set_xlim(xlims)
ax_ind.autoscale(enable=True, axis='y', tight=True)
ax_rad = fig.add_subplot(413)
ax_rad.plot(time*1E6, self.E_rad)
ax_rad.set_ylabel('E_rad (V/m)')
ax_rad.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax_rad.grid(True,which='both')
ax_rad.set_xlim(xlims)
ax_rad.autoscale(enable=True, axis='y', tight=True)
ax = fig.add_subplot(414)
ax.plot(time*1E6, self.E_tot)
ax.set_ylabel('E (V/m)')
ax.set_xlabel('Time ($\mu$s)')
ax.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(True,which='both')
ax.set_xlim(xlims)
ax.autoscale(enable=True, axis='y', tight=True)
### Total E-field
if self.D == 70.0:
meas = 'E_NW60'
elif self.D == 180.0:
meas = 'E_12F'
elif self.D == 315.0:
meas = 'dE_8'
elif self.D == 391.0:
meas = 'dE_9'
elif self.D == 3000.0:
meas = 'E_GOLF'
elif self.D == 42000.0:
meas = 'E_LOG'
fo = pickle.load(open(self.fileSave, "rb"))
E_meas = fo[meas]
t = fo['time']*1E-6
E_meas = E_meas[::int(len(E_meas)/len(t))]
fig = plt.figure()
fig.suptitle('Distance from channel: %d m' % self.D)
ax = fig.add_subplot(111)
ax.plot(t[:int(len(self.E_tot))]*1E6, E_meas[:int(len(self.E_tot))], 'g', label=meas)
ax.plot(time*1E6, self.E_tot, label='E-total')
ax.set_title('$E_{total}$')
ax.set_ylabel('E (V/m)')
ax.set_xlabel('Time ($\mu$s)')
ax.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(True,which='both')
ax.set_xlim(xlims)
ax.autoscale(enable=True, axis='y', tight=True)
plt.legend()
picker = df.pickerPlot(fig,ax)
picker.plot()
#~ plt.figure()
#~ plt.plot(z_array,result)
plt.show()
### E_es
#~ fig = plt.figure()
#~ ax = fig.add_subplot(111)
#~ ax.plot(time*1E6, self.E_es)
#~ ax.set_title('$E_{es}$')
#~ ax.set_ylabel('E (V/m)')
#~ ax.set_xlabel('Time ($\mu$s)')
### E_ind
#~ fig = plt.figure()
#~ ax = fig.add_subplot(111)
#~ ax.plot(time*1E6, self.E_ind)
#~ ax.set_title('$E_{ind}$')
#~ ax.set_ylabel('E (V/m)')
#~ ax.set_xlabel('Time ($\mu$s)')
### E_rad
#~ fig = plt.figure()
#~ ax = fig.add_subplot(111)
#~ ax.plot(time*1E6, self.E_rad)
#~ ax.set_title('$E_{rad}$')
#~ ax.set_ylabel('E (V/m)')
#~ ax.set_xlabel('Time ($\mu$s)')
print(" - Done.")
plt.show()
elif run == 17:
label = r'MTLE Model, $\lambda$ = 10km'
###
ax.plot(run_data['%d'%run]['t_array']['sum']*xfactor, ave*yfactor, '-', label=label)#r'$E^{MTLE}_{tot}$')# -- Run %02d' % run)
ax.set_title(sup_title)
#~ ax.set_xlabel(xlabel)
#~ ax.set_ylabel(ylabel)
ax.set_xlim(xlim)
ax.xaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(True,which='both')
ax.legend(loc=4)
p = df.pickerPlot(fig, ax)
p.plot()
### dE/dt
dE = np.gradient(run_data['%d'%runs[0]]['E_tot']['sum'], np.diff(run_data['%d'%run]['t_array']['rs'])[0])
boost = dE_mult
ylim = [-2, 16]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title(sup_title + ' -- dE/dt (D = %0.1f m)' % dist)
ax.plot(run_data['%d'%run]['t_array']['dE_measured'], moving_average(run_data['%d'%run]['E_tot']['dE_measured'],30)*1E-9, label=r'Measured')
ax.plot(run_data['%d'%run]['t_array']['sum']*xfactor,moving_average(dE,5)*boost*1E-9, label=r'MTLE $\times$ %0.2f' % boost)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
if root == parent:
files = file
for file in files:
print(file)
data = pickle.load(open(parent+file, 'rb'))['II_HI']
t = data.dataTime * 1E-6
i = data.data
__, di, __ = diff.differentiate(t, i)
i = i / np.max(i)
di = di / np.max(di)
di_filtered = filters.moving_average(di)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(t, i, t, di_filtered)
ax.set_title(file[0:6] + '_'+ file[-8:-5] + ' - Normalized and filtered dI/dt (5 pt MA)')
p = df.pickerPlot(fig, ax)
p.plot()
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot(t, i, t, di)
ax1.set_title(file[0:6] + '_'+ file[-8:-5] + '- Normalized raw data')
p1 = df.pickerPlot(fig1, ax1)
p1.plot()
plt.show()
def get_currents(fileSave):
### Generate desired return-stroke waveform.
# Triangular wave used in Uman et al. (1975)
# (t_array_i, i) = cw.triangular(peak=1.0E4, peak_time=1.0E-6, t_end=25E-6, \
# samples=500.0)
#
# t_end_i = t_array_i[-1]
# delta_t = np.diff(t_array_i)[0]
#
# ### Generate the time derivative of the RS current waveform
# di = np.gradient(i, delta_t)
# i_r = i
# ii_real = i
#
# return (t_array_i, delta_t, i, i_r, ii_real, di, t_end_i)
# Current waveshape from Nucci et al. (1990)
#~ I01 = 9.9E3
#~ I02 = 7.5E3
#~
#~ tau_1 = 0.072E-6
#~ tau_2 = 5E-6
#~ tau_3 = 100E-6
#~ tau_4 = 6E-6
#~
#~ eta = 0.845
#~
#~ (t_array_i, i, err) = cw.nucci_1990(I=[I01, I02], tau = [tau_1, tau_2, \
#~ tau_3, tau_4], eta=eta, t_end=t_end_i,\
#~ samples=samples_i)
#~ fileSave = './UF1333_I_RS1.p'
try:
fo = pickle.load(open(currents, "rb"))
i = fo['i']
t_array_i = fo['t_array_i']
i_r = fo['i_r']
ii_real = fo['ii_real']
#~ p = df.RelativeTimePlot(t_array_i, i_r)
#~ p.plot()
#~ plt.show()
delta_t = np.diff(t_array_i)[0]
#~ print(delta_t)
#~ from scipy.signal import savgol_filter
#~ i_r = savgol_filter(i_r, 9, 5)
#~ i_r = i_r[p.zero_ind:-1]
#### FOR UP/DOWN and REF ONLY (i_r must already be negative)
if name != 'rs':
i = i_r
#~ plt.plot(i)
#~ plt.plot(ii_real)
#~ plt.plot(i_r)
#~ plt.show()
#~ sys.exit(1)
except FileNotFoundError:
print('File not found: \' %s \'' % currents)
try:
fo = pickle.load(open(fileSave, "rb"))
i = fo['i_model']
t_array_i = fo['time']
i_r = -0.5*fo['i_r']
ii_real = fo['ii_hi']
ind = fo['ind']
delta_t = np.diff(t_array_i)[0]
#~ print(delta_t)
#~ p = df.RelativeTimePlot(t_array_i, i)
#~ p.plot()
#~ plt.show()
#~ i = i[p.zero_ind:-1]# - i[p.zero_ind]
#~ i = i[ind-int(fileSaveCurrentsDelay/delta_t):-1]
p = df.RelativeTimePlot(t_array_i, i_r)
p.plot()
plt.show()
from scipy.signal import savgol_filter
i_r = savgol_filter(i_r, 9, 5)
i_r = i_r[p.zero_ind:-1]# - i_r[p.zero_ind]
plt.plot(i)
plt.plot(ii_real)
p = df.pickerPlot(plt.gcf(), plt.gca())
p.plot()
plt.show()
data = {}
data['i'] = i
data['t_array_i'] = t_array_i
data['i_r'] = i_r
data['ii_real'] = ii_real
pickle.dump(data, open(currents, 'wb'))
#~ plt.plot(i)
#~ plt.plot(ii_real)
#~ plt.plot(i_r)
#~ plt.show()
#~ sys.exit(1)
except FileNotFoundError:
print('File not found: \' %s \'' % fileSave)
raise
t_end_i = t_array_i[-1]
delta_t = np.diff(t_array_i)[0]
### Generate the time derivative of the RS current waveform
di = np.gradient(i, delta_t)
return (t_array_i, delta_t, i, i_r, ii_real, di, t_end_i)
