def get_data(self, lims=[5E-6, 295E-6], wantOffset=False):
if "yoko" in self.scope_type.lower():
self.read_yoko(wantOffset)
elif "lecroy" in self.scope_type.lower():
self.read_lecroy(wantOffset)
else:
raise IOError('Unknown scope type.')
# Smooth out LOG and GOLF data
if "log" in self.measurement_name.lower() or \
"golf" in self.measurement_name.lower():
window_length = 5.0
sma = filters.moving_average(self.data, window_length)
self.data = sma
# Integrate desired dE/dt waveforms
if "int" in self.measurement_name.lower():
self.data = integrators.integrate_cumtrapz(y=self.data,
x=self.dataTime,
initial=0)
self.get_plot(lims)
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()
for root, dir, file in os.walk(parent):
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()