plt.plot(t_vec, i_vec, color=lcmaps.get_color(0))
lplot.save_plt(plt, "cs_ap_scaled_with_current", ".")
plt.close()
print "plotting scaled ap"
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
ax.set_ylabel(r"Mem. Pot. \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Time \textbf{[\si{\milli\second}]}")
plt.plot(t_vec, v_vec, color=lcmaps.get_color(0))
lplot.save_plt(plt, "cs_ap_scaled", ".")
plt.close()
freqs, amps, phase = \
de.find_freq_and_fft(p['dt'],v_vec, length=v_vec.shape[-1]*1, f_cut=3)
# Remove the first coefficient as we don't care about the baseline.
freqs = np.delete(freqs, 0)
amps = np.delete(amps, 0)
print "plotting fourier"
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
ax.set_ylabel(r"Mem. Pot. \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Frequency \textbf{[\si{\kilo\hertz}]}")
plt.plot(freqs, amps, color=lcmaps.get_color(0))
lplot.save_plt(plt, "cs_ap_fourier", ".")
plt.close()
# Save the actionpotential to file.
def plot(self, dir_plot):
"""
Plotting stats about the spikes.
"""
# pylint: disable=too-many-locals
data = self.data
run_param = self.run_param
# String to put before output to the terminal.
str_start = self.name
str_start += " "*(20 - len(self.name)) + ":"
# Set global matplotlib parameters.
LFPy_util.plot.set_rc_param()
# Plot spike amps I {{{1 #
fname = self.name + '_spike_amps_I'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['r_vec'],
data['amps_I_mean'],
color=lcmaps.get_color(0),
marker='o',
markersize=5,
)
ax.fill_between(data['r_vec'],
data['amps_I_mean'] - data['amps_I_std'],
data['amps_I_mean'] + data['amps_I_std'],
color=lcmaps.get_color(0),
alpha=0.2)
ax.set_ylabel(r"Amplitude \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Distance from Soma \textbf{[\si{\micro\metre}]}")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# }}} #
# {{{ Plot spike amps I log
fname = self.name + '_spike_amps_I_log'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['r_vec'],
data['amps_I_mean'],
color=lcmaps.get_color(0),
marker='o',
markersize=5,
)
ax.fill_between(data['r_vec'],
data['amps_I_mean'] - data['amps_I_std'],
data['amps_I_mean'] + data['amps_I_std'],
color=lcmaps.get_color(0),
alpha=0.2)
# Ugly way to put in some graphs for power laws.
# Left side.
x0 = data['r_vec'][0]
x1 = data['r_vec'][1]
y0 = data['amps_I_mean'][0]
for p in [1.0, 2.0, 3.0]:
y1 = np.power( 1.0 / data['r_vec'][1], p) * \
data['amps_I_mean'][ 0] / \
np.power(1.0 / data['r_vec'][0], p)
ax.plot([x0, x1], [y0, y1], color='black')
ax.annotate(
'-'+str(int(p)),
xy=(x1,y1),
xytext=(x1*1.01, y1*0.95),
)
# Right side.
x0 = data['r_vec'][-3]
x1 = data['r_vec'][-1]
y1 = data['amps_I_mean'][-1]
for p in [1.0, 2.0, 3.0]:
y0 = np.power(1.0 / data['r_vec'][-3], p) * \
data['amps_I_mean'][-1] / \
np.power(1.0 / data['r_vec'][-1], p)
ax.plot([x0, x1], [y0, y1], color='black')
ax.annotate(
'-'+str(int(p)),
xy=(x0,y0),
xytext=(x0*0.99, y0*0.95),
horizontalalignment='right',
)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim([data['r_vec'].min(), data['r_vec'].max()])
ticker = mpl.ticker.MaxNLocator(nbins=7)
ax.xaxis.set_major_locator(ticker)
ax.xaxis.get_major_formatter().labelOnlyBase = False
# ticker = mpl.ticker.MaxNLocator(nbins=7)
# ax.yaxis.set_major_locator(ticker)
# ax.yaxis.get_major_formatter().labelOnlyBase = False
# Set a label formatter to use normal numbers.
ax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_ylabel(r"Amplitude \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Distance from Soma \textbf{[\si{\micro\metre}]}")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# }}}
# Plot spike amps II {{{1 #
fname = self.name + '_spike_amps_II'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['r_vec'],
data['amps_II_mean'],
color=lcmaps.get_color(0),
marker='o',
markersize=5,
)
ax.fill_between(data['r_vec'],
data['amps_II_mean'] - data['amps_II_std'],
data['amps_II_mean'] + data['amps_II_std'],
color=lcmaps.get_color(0),
alpha=0.2)
ax.set_ylabel("Amplitude")
ax.set_xlabel("Distance from Soma")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# {{{ Plot spike amps II log
fname = self.name + '_spike_amps_II_log'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['r_vec'],
data['amps_II_mean'],
color=lcmaps.get_color(0),
marker='o',
markersize=5,
)
ax.fill_between(data['r_vec'],
data['amps_II_mean'] - data['amps_II_std'],
data['amps_II_mean'] + data['amps_II_std'],
color=lcmaps.get_color(0),
alpha=0.2)
# Ugly way to put in some graphs for power laws.
# Left side.
x0 = data['r_vec'][0]
x1 = data['r_vec'][1]
y0 = data['amps_II_mean'][0]
for p in [1.0, 2.0, 3.0]:
y1 = np.power( 1.0 / data['r_vec'][1], p) * \
data['amps_II_mean'][ 0] / \
np.power(1.0 / data['r_vec'][0], p)
ax.plot([x0, x1], [y0, y1], color='black')
ax.annotate(
'-'+str(int(p)),
xy=(x1,y1),
xytext=(x1*1.01, y1*0.95),
)
# Right side.
x0 = data['r_vec'][-3]
x1 = data['r_vec'][-1]
y1 = data['amps_II_mean'][-1]
for p in [1.0, 2.0, 3.0]:
y0 = np.power(1.0 / data['r_vec'][-3], p) * \
data['amps_II_mean'][-1] / \
np.power(1.0 / data['r_vec'][-1], p)
ax.plot([x0, x1], [y0, y1], color='black')
ax.annotate(
'-'+str(int(p)),
xy=(x0,y0),
xytext=(x0*0.99, y0*0.95),
horizontalalignment='right',
)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim([data['r_vec'].min(), data['r_vec'].max()])
ticker = mpl.ticker.MaxNLocator(nbins=7)
ax.xaxis.set_major_locator(ticker)
ax.xaxis.get_major_formatter().labelOnlyBase = False
# ticker = mpl.ticker.MaxNLocator(nbins=7)
# ax.yaxis.set_major_locator(ticker)
# ax.yaxis.get_major_formatter().labelOnlyBase = False
# Set a label formatter to use normal numbers.
ax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_ylabel(r"Amplitude \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Distance from Soma \textbf{[\si{\micro\metre}]}")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# }}}
# Plot spike width I {{{1 #
fname = self.name + '_spike_width_I'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['r_vec'],
data['widths_I_mean'],
color=lcmaps.get_color(0),
marker='o',
markersize=5,
)
ax.fill_between(data['r_vec'],
data['widths_I_mean'] - data['widths_I_std'],
data['widths_I_mean'] + data['widths_I_std'],
color=lcmaps.get_color(0),
alpha=0.2)
ax.set_ylabel("Spike Width")
ax.set_xlabel("Distance from Soma")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot spike widths II {{{1 #
fname = self.name + '_spike_width_II'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['r_vec'],
data['widths_II_mean'],
color=lcmaps.get_color(0),
marker='o',
markersize=5,
)
ax.fill_between(
data['r_vec'],
data['widths_II_mean'] - data['widths_II_std'],
data['widths_II_mean'] + data['widths_II_std'],
color=lcmaps.get_color(0),
alpha=0.2)
ax.set_ylabel("Spike Width")
ax.set_xlabel("Distance from Soma")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot spike widths III {{{1 #
fname = self.name + '_spike_width_III'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['r_vec'],
data['widths_III_mean'],
color=lcmaps.get_color(0),
marker='o',
markersize=5,
)
ax.fill_between(
data['r_vec'],
data['widths_III_mean'] - data['widths_III_std'],
data['widths_III_mean'] + data['widths_III_std'],
color=lcmaps.get_color(0),
alpha=0.2)
ax.set_ylabel("Spike Width")
ax.set_xlabel("Distance from Soma")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot morphology xz {{{ #
LFPy_util.plot.morphology(data['poly_morph_xz'],
data['poly_morph_axon_xz'],
elec_x=data['elec_x'],
elec_y=data['elec_z'],
fig_size=lplot.size_square,
fname=self.name + "_morph_elec_xz",
plot_save_dir=dir_plot,
x_label='x',
y_label='z',
show=False)
# }}} #
# Spike to plot.
elec_index = run_param['n']/2
# title_str = r"Distance from Soma = \SI{{{}}}{{\micro\metre}}"
# title_str = title_str.format(round(data['r_vec'][elec_index]),2)
c = lcmaps.get_short_color_array(2 + 1)
# Plot middle electrode spike {{{1 #
fname = self.name + '_middle_elec_spike'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['spikes_t_vec'],
data['spikes'][elec_index],
color=c[0])
# Trace I
plt.plot(data['spikes_t_vec'],
data['widths_I_trace'][elec_index],
color=c[1],
)
# Trace II
plt.plot(data['spikes_t_vec'],
data['widths_II_trace'][elec_index],
color=c[1])
# plt.title(title_str)
# Save plt.
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot middle electrode spike freq {{{1 #
fname = self.name + '_middle_elec_spike_fourier'
freq, amp, phase = de.find_freq_and_fft(
data['dt'],
data['spikes'][elec_index],
)
# Remove the first coefficient as we don't care about the baseline.
freq = np.delete(freq, 0)
amp = np.delete(amp, 0)
# Delete frequencies above the option.
if self.plot_param['freq_end'] is not None:
idx = min(
range(len(freq)),
key=lambda i: abs(freq[i] - self.plot_param['freq_end'])
)
freq = freq[0:idx]
amp = amp[0:idx]
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
plt.plot(freq, amp, color=lcmaps.get_color(0))
# plt.title(title_str)
ax.set_ylabel(r'Amplitude \textbf[$\mathbf{mV}$\textbf]')
ax.set_xlabel(r'Frequency \textbf[$\mathbf{kHz}$\textbf]')
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot middle electrode signal {{{1 #
fname = self.name + '_middle_elec'
print "plotting :", fname
c = lcmaps.get_short_color_array(2 + 1)
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['t_vec'],
data['LFP'][elec_index],
color=lcmaps.get_color(0))
# plt.title(title_str)
# Save plt.
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot middle electrode signal freq {{{1 #
fname = self.name + '_middle_elec_fourier'
freq, amp, phase = de.find_freq_and_fft(
data['dt'],
data['LFP'][elec_index],
)
# Remove the first coefficient as we don't care about the baseline.
freq = np.delete(freq, 0)
amp = np.delete(amp, 0)
if self.plot_param['freq_end'] is not None:
idx = min(
range(len(freq)),
key=lambda i: abs(freq[i] - self.plot_param['freq_end'])
)
freq = freq[0:idx]
amp = amp[0:idx]
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
plt.plot(freq, amp, color=lcmaps.get_color(0))
ax.set_ylabel(r'Amplitude \textbf[$\mathbf{mV}$\textbf]')
ax.set_xlabel(r'Frequency \textbf[$\mathbf{kHz}$\textbf]')
# plt.title(title_str)
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
if self.plot_param['plot_detailed']:
# Create the directory if it does not exist.
sub_dir = os.path.join(dir_plot, self.name + "_detailed")
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
# t = len(run_param['theta'])
# p = run_param['n_phi']
p = 1
n = run_param['n']
# This title string should be formatted.
title_str = r"Distance from Soma = \SI{{{}}}{{\micro\metre}}"
cnt = 0
for j in xrange(p):
for k in xrange(n):
title_str_1 = title_str.format(data['r_vec'][k])
# Plot all elec spikes {{{1 #
fname = self.name + '_elec_p_{}_n_{}'.format(j * 360 / p, k)
print "plotting :", fname
c = lcmaps.get_short_color_array(2 + 1)
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['spikes_t_vec'],
data['spikes'][cnt],
color=c[0])
# Trace I
plt.plot(data['spikes_t_vec'],
data['widths_I_trace'][cnt],
color=c[1],
)
# Trace II
plt.plot(data['spikes_t_vec'],
data['widths_II_trace'][cnt],
color=c[1])
# Trace III
plt.plot(data['spikes_t_vec'],
data['widths_III_trace'][cnt],
color=c[1])
# plt.title(title_str_1)
# Save plt.
lplot.save_plt(plt, fname, sub_dir)
plt.close()
# 1}}} #
# Plot all elec spikes freq {{{1 #
# Fourier plot.
fname = self.name + '_freq_elec_p_{}_n_{}'.format(j * 360 / p, k)
freq, amp, phase = de.find_freq_and_fft(
data['dt'],
data['spikes'][cnt],
)
# Remove the first coefficient as we don't care about the baseline.
freq = np.delete(freq, 0)
amp = np.delete(amp, 0)
if self.plot_param['freq_end'] is not None:
idx = min(
range(len(freq)),
key=lambda i: abs(freq[i] - self.plot_param['freq_end'])
)
freq = freq[0:idx]
amp = amp[0:idx]
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
plt.plot(freq, amp, color=c[0])
ax.set_ylabel(r'Amplitude \textbf[$\mathbf{mV}$\textbf]')
ax.set_xlabel(r'Frequency \textbf[$\mathbf{kHz}$\textbf]')
# plt.title(title_str_1)
lplot.save_plt(plt, fname, sub_dir)
plt.close()
# 1}}} #
cnt += 1
def plot(self, dir_plot):
"""
Plotting stats about the spikes.
"""
# pylint: disable=too-many-locals
data = self.data
run_param = self.run_param
# String to put before output to the terminal.
str_start = self.name
str_start += " "*(20 - len(self.name)) + ":"
# Set global matplotlib parameters.
LFPy_util.plot.set_rc_param()
# 3D plot {{{1 #
fname = self.name + "_elec_pos"
c = lplot.get_short_color_array(5)[2]
fig = plt.figure(figsize=lplot.size_common)
ax = fig.add_subplot(111, projection='3d')
cnt = 0
for i, theta in enumerate(run_param['theta']):
for j in xrange(run_param['n_phi']):
for k in xrange(run_param['n']):
ax.scatter(data['elec_x'][cnt],
data['elec_y'][cnt],
data['elec_z'][cnt],
c=c)
cnt += 1
ax.set_xlim(-run_param['R'], run_param['R'])
ax.set_ylim(-run_param['R'], run_param['R'])
ax.set_zlim(-run_param['R'], run_param['R'])
ax.set_xlabel("X axis")
ax.set_ylabel("Y axis")
ax.set_zlabel("Z axis")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot spike amps I {{{1 #
fname = self.name + '_spike_amps_I'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
c = lplot.get_short_color_array(data['amps_I_mean'].shape[0] + 1)
for t in xrange(data['amps_I_mean'].shape[0]):
label = r'$\theta = {}\degree$'.format(run_param['theta'][t])
plt.plot(data['r_vec'],
data['amps_I_mean'][t],
color=c[t],
marker='o',
markersize=5,
label=label)
ax.fill_between(data['r_vec'],
data['amps_I_mean'][t] - data['amps_I_std'][t],
data['amps_I_mean'][t] + data['amps_I_std'][t],
color=c[t],
alpha=0.2)
handles, labels = ax.get_legend_handles_labels()
# Position the legen on the right side of the plot.
ax.legend(handles,
labels,
loc='upper left',
bbox_to_anchor=(1.0, 1.04), )
ax.set_ylabel(r"Amplitude \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Distance from Soma \textbf{[\si{\micro\metre}]}")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# }}} #
# {{{ Plot spike amps I log
fname = self.name + '_spike_amps_I_log'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
c = lplot.get_short_color_array(data['amps_I_mean'].shape[0] + 1)
for t in xrange(data['amps_I_mean'].shape[0]):
label = r'$\theta = {}\degree$'.format(run_param['theta'][t])
plt.plot(data['r_vec'],
data['amps_I_mean'][t],
color=c[t],
marker='o',
markersize=5,
label=label)
ax.fill_between(data['r_vec'],
data['amps_I_mean'][t] - data['amps_I_std'][t],
data['amps_I_mean'][t] + data['amps_I_std'][t],
color=c[t],
alpha=0.2)
# # Ugly way to put in some graphs for power laws.
# # Left side.
# x0 = data['r_vec'][0]
# x1 = data['r_vec'][1]
# y0 = data['amps_I_mean'][t, 0]
# for p in [1.0, 2.0, 3.0]:
# y1 = np.power( 1.0 / data['r_vec'][1], p) * \
# data['amps_I_mean'][t, 0] / \
# np.power(1.0 / data['r_vec'][0], p)
# ax.plot([x0, x1], [y0, y1], color='black')
# # Right side.
# x0 = data['r_vec'][-3]
# x1 = data['r_vec'][-1]
# y1 = data['amps_I_mean'][t,-1]
# for p in [1.0, 2.0, 3.0]:
# y0 = np.power(1.0 / data['r_vec'][-3], p) * \
# data['amps_I_mean'][t, -1] / \
# np.power(1.0 / data['r_vec'][-1], p)
# ax.plot([x0, x1], [y0, y1], color='black')
handles, labels = ax.get_legend_handles_labels()
# Position the legen on the right side of the plot.
ax.legend(handles,
labels,
loc='upper left',
bbox_to_anchor=(1.0, 1.04), )
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim([data['r_vec'].min(), data['r_vec'].max()])
ticker = mpl.ticker.MaxNLocator(nbins=7)
ax.xaxis.set_major_locator(ticker)
ax.xaxis.get_major_formatter().labelOnlyBase = False
# ticker = mpl.ticker.MaxNLocator(nbins=7)
# ax.yaxis.set_major_locator(ticker)
# ax.yaxis.get_major_formatter().labelOnlyBase = False
# Set a label formatter to use normal numbers.
ax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_ylabel(r"Amplitude \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Distance from Soma \textbf{[\si{\micro\metre}]}")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# }}}
# Plot spike amps II {{{1 #
fname = self.name + '_spike_amps_II'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
c = lplot.get_short_color_array(data['amps_II_mean'].shape[0] + 1)
for t in xrange(data['amps_II_mean'].shape[0]):
label = r'$\theta = {}\degree$'.format(run_param['theta'][t])
plt.plot(data['r_vec'],
data['amps_II_mean'][t],
color=c[t],
marker='o',
markersize=5,
label=label)
ax.fill_between(data['r_vec'],
data['amps_II_mean'][t] - data['amps_II_std'][t],
data['amps_II_mean'][t] + data['amps_II_std'][t],
color=c[t],
alpha=0.2)
handles, labels = ax.get_legend_handles_labels()
# Position the legen on the right side of the plot.
ax.legend(handles,
labels,
loc='upper left',
bbox_to_anchor=(1.0, 1.04), )
ax.set_ylabel("Amplitude")
ax.set_xlabel("Distance from Soma")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# {{{ Plot spike amps II log
fname = self.name + '_spike_amps_II_log'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
c = lplot.get_short_color_array(data['amps_II_mean'].shape[0] + 1)
for t in xrange(data['amps_II_mean'].shape[0]):
label = r'$\theta = {}\degree$'.format(run_param['theta'][t])
plt.plot(data['r_vec'],
data['amps_II_mean'][t],
color=c[t],
marker='o',
markersize=5,
label=label)
ax.fill_between(data['r_vec'],
data['amps_II_mean'][t] - data['amps_II_std'][t],
data['amps_II_mean'][t] + data['amps_II_std'][t],
color=c[t],
alpha=0.2)
handles, labels = ax.get_legend_handles_labels()
# Position the legen on the right side of the plot.
ax.legend(handles,
labels,
loc='upper left',
bbox_to_anchor=(1.0, 1.04), )
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlim([data['r_vec'].min(), data['r_vec'].max()])
ticker = mpl.ticker.MaxNLocator(nbins=7)
ax.xaxis.set_major_locator(ticker)
ax.xaxis.get_major_formatter().labelOnlyBase = False
# ticker = mpl.ticker.MaxNLocator(nbins=7)
# ax.yaxis.set_major_locator(ticker)
# ax.yaxis.get_major_formatter().labelOnlyBase = False
# Set a label formatter to use normal numbers.
ax.xaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.yaxis.set_major_formatter(mpl.ticker.ScalarFormatter())
ax.set_ylabel(r"Amplitude \textbf{[\si{\milli\volt}]}")
ax.set_xlabel(r"Distance from Soma \textbf{[\si{\micro\metre}]}")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# }}}
# Plot spike width I {{{1 #
fname = self.name + '_spike_width_I'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
c = lplot.get_short_color_array(data['widths_I_mean'].shape[0] + 1)
for t in xrange(data['widths_I_mean'].shape[0]):
label = r'$\theta = {}\degree$'.format(run_param['theta'][t])
plt.plot(data['r_vec'],
data['widths_I_mean'][t],
color=c[t],
marker='o',
markersize=5,
label=label)
ax.fill_between(data['r_vec'],
data['widths_I_mean'][t] - data['widths_I_std'][t],
data['widths_I_mean'][t] + data['widths_I_std'][t],
color=c[t],
alpha=0.2)
handles, labels = ax.get_legend_handles_labels()
# Position the legen on the right side of the plot.
ax.legend(handles,
labels,
loc='upper left',
bbox_to_anchor=(1.0, 1.04), )
ax.set_ylabel("Spike Width")
ax.set_xlabel("Distance from Soma")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot spike widths II {{{1 #
fname = self.name + '_spike_width_II'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
c = lplot.get_short_color_array(data['widths_II_mean'].shape[0] + 1)
for t in xrange(data['widths_II_mean'].shape[0]):
label = r'$\theta = {}\degree$'.format(run_param['theta'][t])
plt.plot(data['r_vec'],
data['widths_II_mean'][t],
color=c[t],
marker='o',
markersize=5,
label=label)
ax.fill_between(
data['r_vec'],
data['widths_II_mean'][t] - data['widths_II_std'][t],
data['widths_II_mean'][t] + data['widths_II_std'][t],
color=c[t],
alpha=0.2)
handles, labels = ax.get_legend_handles_labels()
# Position the legen on the right side of the plot.
ax.legend(handles,
labels,
loc='upper left',
bbox_to_anchor=(1.0, 1.04), )
ax.set_ylabel("Spike Width")
ax.set_xlabel("Distance from Soma")
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot morphology {{{1 #
LFPy_util.plot.morphology(data['poly_morph'],
data['poly_morph_axon'],
elec_x=data['elec_x'],
elec_y=data['elec_y'],
fig_size=lplot.size_common,
fname=self.name + "_morph_elec_xy",
plot_save_dir=dir_plot,
show=False)
# 1}}} #
# Plot morphology xz {{{ #
LFPy_util.plot.morphology(data['poly_morph_xz'],
data['poly_morph_axon_xz'],
elec_x=data['elec_x'],
elec_y=data['elec_z'],
fig_size=lplot.size_common,
fname=self.name + "_morph_elec_xz",
plot_save_dir=dir_plot,
show=False)
# }}} #
# Spike to plot.
elec_index = run_param['n']/2
# title_str = r"Distance from Soma = \SI{{{}}}{{\micro\metre}}"
# title_str = title_str.format(round(data['r_vec'][elec_index]),2)
c = lplot.get_short_color_array(2 + 1)
# Plot middle electrode spike {{{1 #
fname = self.name + '_middle_elec_spike'
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['spikes_t_vec'],
data['spikes'][elec_index],
color=c[0])
# Trace I
plt.plot(data['spikes_t_vec'],
data['widths_I_trace'][elec_index],
color=c[1],
)
# Trace II
plt.plot(data['spikes_t_vec'],
data['widths_II_trace'][elec_index],
color=c[1])
# plt.title(title_str)
# Save plt.
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot middle electrode spike freq {{{1 #
fname = self.name + '_middle_elec_spike_fourier'
freq, amp, phase = de.find_freq_and_fft(
data['dt'],
data['spikes'][elec_index],
)
# Remove the first coefficient as we don't care about the baseline.
freq = np.delete(freq, 0)
amp = np.delete(amp, 0)
# Delete frequencies above the option.
if self.plot_param['freq_end'] is not None:
idx = min(
range(len(freq)),
key=lambda i: abs(freq[i] - self.plot_param['freq_end'])
)
freq = freq[0:idx]
amp = amp[0:idx]
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
plt.plot(freq, amp, color=c[0])
# plt.title(title_str)
ax.set_ylabel(r'Amplitude \textbf[$\mathbf{mV}$\textbf]')
ax.set_xlabel(r'Frequency \textbf[$\mathbf{kHz}$\textbf]')
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot middle electrode signal {{{1 #
fname = self.name + '_middle_elec'
print "plotting :", fname
c = lplot.get_short_color_array(2 + 1)
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['t_vec'],
data['LFP'][elec_index],
color=c[0])
# plt.title(title_str)
# Save plt.
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
# Plot middle electrode signal freq {{{1 #
fname = self.name + '_middle_elec_fourier'
freq, amp, phase = de.find_freq_and_fft(
data['dt'],
data['LFP'][elec_index],
)
# Remove the first coefficient as we don't care about the baseline.
freq = np.delete(freq, 0)
amp = np.delete(amp, 0)
if self.plot_param['freq_end'] is not None:
idx = min(
range(len(freq)),
key=lambda i: abs(freq[i] - self.plot_param['freq_end'])
)
freq = freq[0:idx]
amp = amp[0:idx]
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
plt.plot(freq, amp, color=c[0])
ax.set_ylabel(r'Amplitude \textbf[$\mathbf{mV}$\textbf]')
ax.set_xlabel(r'Frequency \textbf[$\mathbf{kHz}$\textbf]')
# plt.title(title_str)
lplot.save_plt(plt, fname, dir_plot)
plt.close()
# 1}}} #
if self.plot_param['plot_detailed']:
# Create the directory if it does not exist.
sub_dir = os.path.join(dir_plot, self.name + "_detailed")
if not os.path.exists(sub_dir):
os.makedirs(sub_dir)
t = len(run_param['theta'])
# p = run_param['n_phi']
p = 1
n = run_param['n']
# This title string should be formatted.
title_str = r"Distance from Soma = \SI{{{}}}{{\micro\metre}}"
cnt = 0
for i in xrange(t):
for j in xrange(p):
for k in xrange(n):
title_str_1 = title_str.format(data['r_vec'][k])
# Plot all elec spikes {{{1 #
fname = self.name + '_elec_t_{}_p_{}_n_{}'.format(
run_param['theta'][i], j * 360 / p, k)
print "plotting :", fname
c = lplot.get_short_color_array(2 + 1)
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
# Plot
plt.plot(data['spikes_t_vec'],
data['spikes'][cnt],
color=c[0])
# Trace I
plt.plot(data['spikes_t_vec'],
data['widths_I_trace'][cnt],
color=c[1],
)
# Trace II
plt.plot(data['spikes_t_vec'],
data['widths_II_trace'][cnt],
color=c[1])
# plt.title(title_str_1)
# Save plt.
lplot.save_plt(plt, fname, sub_dir)
plt.close()
# 1}}} #
# Plot all elec spikes freq {{{1 #
# Fourier plot.
fname = self.name + '_freq_elec_t_{}_p_{}_n_{}'.format(
run_param['theta'][i], j * 360 / p, k)
freq, amp, phase = de.find_freq_and_fft(
data['dt'],
data['spikes'][cnt],
)
# Remove the first coefficient as we don't care about the baseline.
freq = np.delete(freq, 0)
amp = np.delete(amp, 0)
if self.plot_param['freq_end'] is not None:
idx = min(
range(len(freq)),
key=lambda i: abs(freq[i] - self.plot_param['freq_end'])
)
freq = freq[0:idx]
amp = amp[0:idx]
print "plotting :", fname
plt.figure(figsize=lplot.size_common)
ax = plt.gca()
lplot.nice_axes(ax)
plt.plot(freq, amp, color=c[0])
ax.set_ylabel(r'Amplitude \textbf[$\mathbf{mV}$\textbf]')
ax.set_xlabel(r'Frequency \textbf[$\mathbf{kHz}$\textbf]')
# plt.title(title_str_1)
lplot.save_plt(plt, fname, sub_dir)
plt.close()
# 1}}} #
cnt += 1
