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plot.py
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'''
@author: Rapp & Braun
'''
import sys
sys.path.insert(0, "C:/nrn/lib/python")
import numpy as np
import matplotlib.pyplot as plt
from neuron import h
from Axon_Models import mrg_axon, hh_axon, simple_axon
from matplotlib.ticker import FormatStrFormatter
def plot_traces(name, trace_list, time_axis, stimulus, trace_height=100):
trace_list = np.array(trace_list)
# MAX_V = np.ones(30)*1000
axes = plt.figure().add_axes([0.125, 0.125, 0.75, 0.8])
# b=0
for plt_it, voltage_trace in enumerate(trace_list):
stimulus_trace = stimulus is not None
y_lim = trace_height * (len(trace_list) + 5 * int(stimulus_trace))
if stimulus_trace:
y_step = trace_height * (plt_it + 5)
else:
y_step = trace_height * (plt_it + 1)
plt.plot(time_axis, [y_lim - y_step for j in time_axis], 'k--', alpha=0.5, linewidth=1)
line = plt.plot(time_axis, voltage_trace[:len(time_axis)] + y_lim - y_step, '-b')
# MAX_V[b] = max(voltage_trace[:len(time_axis)])
# b=b+1
if stimulus is not None:
zipped = np.array(list(zip(time_axis, stimulus)))
x = zipped[:, 0]
y = zipped[:, 1]
plt.plot(x, y / max(abs(y)) * 100 + y_lim - 2 * trace_height)
plt.yticks([y_lim - 2 * trace_height], ['Stimulus'], fontsize=12)
else:
if len(trace_list) > 1:
plt.yticks('')
else:
plt.ylabel('Membrane Potential ($mV$)')
if len(trace_list) > 1:
middle_y = (axes.get_ylim()[1] - axes.get_ylim()[0]) / 2.
slightly_left = (axes.get_xlim()[1] - axes.get_xlim()[0]) * 0.05 * -1
plt.text(slightly_left, middle_y, 'Membrane Potential along Cell', rotation=90, ha='center', va='center')
# print min(MAX_V)
# ===============================================================================
# scalebar
# ===============================================================================
x_loc = plt.gca().get_xlim()[1] * 0.85
y_loc = y_lim - 5 * trace_height + 35
plt.plot([x_loc, x_loc], [y_loc, y_loc + 100], 'k-_', linewidth=1)
plt.text(x_loc, y_loc + 50, ' 100 mV', ha='left', va='center', fontsize=12)
plt.title(name)
plt.xlabel('Time ($ms$)')
# plt.xlim(0, max(time_axis))
# plt.ylim(-50, y_lim)
return axes
def plot_traces_and_field(name, time_axis, stimulus, model, trace_height=100):
trace_list = np.array(model.potential_vector_node_list)
# MAX_V = np.ones(30)*1000
fig = plt.figure()
axes2 = fig.add_axes([0.85, 0.125, 0.09, 0.8])
axes = fig.add_axes([0.125, 0.125, 0.65, 0.8])
# b=0
e_field = []
for sec, e_field_value in zip(model.sections, model.e_field_along_axon):
if type(sec) == mrg_axon.Node or type(sec) == simple_axon.Node or type(sec) == hh_axon.Node:
e_field.append(e_field_value)
# axes2.plot(e_field[0::10], range(10*len(e_field))[-1::-100], 'g')
axes2.plot(e_field, range(100 * len(e_field))[-1::-100], 'darkgray')
# plt.tight_layout()
n = 5
for plt_it, voltage_trace in enumerate(trace_list[::n]):
stimulus_trace = stimulus is not None
y_lim = trace_height * (len(trace_list) + 5 * int(stimulus_trace))
if stimulus_trace:
y_step = trace_height * (n*plt_it + 5)
else:
y_step = trace_height * (n*plt_it + 1)
axes.plot(time_axis, [y_lim - y_step for j in time_axis], 'k--', alpha=0.5, linewidth=1)
line = axes.plot(time_axis, voltage_trace[:len(time_axis)] + y_lim - y_step)
axes2.plot(time_axis, [y_lim - y_step for j in time_axis], 'w', alpha=0.5, linewidth=1)
line2 = axes2.plot(e_field[n*plt_it], y_lim - y_step, '-x')
# line2 = axes2.plot(e_field[plt_it], y_lim - y_step, '-x')
# MAX_V[b] = max(voltage_trace[:len(time_axis)])
# b=b+1
if stimulus is not None:
zipped = np.array(list(zip(time_axis, stimulus)))
x = zipped[:, 0]
y = zipped[:, 1]
axes.plot(x, y / max(abs(y)) * 100 + y_lim - 2 * trace_height)
axes2.plot(x, y / max(abs(y)) * 100 + y_lim - 2 * trace_height, 'w')
axes.set_xticks([y_lim - 2 * trace_height])
axes.set_xticklabels(['Stimulus'])
else:
if len(trace_list) > 1:
axes.set_yticks('')
else:
axes.set_ylabel('Membrane Potential ($mV$)')
if len(trace_list) > 1:
middle_y = (axes.get_ylim()[1] - axes.get_ylim()[0]) / 2.
slightly_left = (axes.get_xlim()[1] - axes.get_xlim()[0]) * 0.05 * -1.5
axes.text(slightly_left, middle_y, 'Membrane Potential along Cell', rotation=90, ha='center', va='center')
# print min(MAX_V)
# a = np.arange(trace_list.shape[0], 0, n)
# axes.set_yticks(np.arange(trace_list.shape[0], 0, -1))
# ticks = axes.get_yticks()
# axes.set_yticklabels(round(ticks / plt_it))
# axes.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# ===============================================================================
# scalebar
# ===============================================================================
x_loc = plt.gca().get_xlim()[1] * 0.85
y_loc = y_lim - 5 * trace_height + 35
axes.plot([x_loc, x_loc], [y_loc, y_loc + 100], 'k-_', linewidth=1)
axes.text(x_loc, y_loc + 50, ' 100 mV', ha='left', va='center', fontsize=12)
axes2.plot([x_loc, x_loc], [y_loc, y_loc + 100], 'w', linewidth=1)
# axes2.text(x_loc, y_loc + 50, ' 100 mV', ha='left', va='center', fontsize=12)
axes.set_title(name)
axes2.set_title('E-Field')
plt.xlabel('Time ($ms$)')
plt.xlim(0, max(time_axis))
plt.ylim(-50, y_lim)
axes.yaxis.set_visible(False)
axes2.yaxis.set_visible(False)
# e_field = []
# for sec, e_field_value in zip(model.sections, model.e_field_list):
# if type(sec) == mrg_axon.Node or type(sec) == simple_axon.Node or type(sec) == hh_axon.Node:
# e_field.append(e_field_value)
#
# axes2 = fig.add_axes([0.90, 0.125, 0.09, 0.7])
# axes2.plot(e_field[0::10], range(10*len(e_field))[0::100], '-x')
# plt.tight_layout()
return axes, axes2
def plot_e_field_along_nerve(e_field_along_nerve):
fig = plt.Figure()
ax1 = fig.add_subplot(111)
ax1.plot(e_field_along_nerve)
ax1.set_xlabel('Compartments')
ax1.set_ylabel('E-field in V/m')
return fig
def plot_potential_along_nerve(potential_along_nerve):
fig = plt.Figure()
ax1 = fig.add_subplot(111)
ax1.plot(potential_along_nerve)
ax1.set_xlabel('Compartments')
ax1.set_ylabel('Potential in V')
return fig
def plot_axon_xy_coordinates(axon):
fig = plt.Figure()
ax1 = fig.add_subplot(111)
ax1.plot(axon.x, axon.y)
ax1.set_xlabel('x in µm')
ax1.set_ylabel('y in µm')
return fig
def plot_axon_xy_coordinates_with_nodes(axon, internode_segments):
# This works only for MHH model, whith nseg_internode=x and nseg_node=1
fig = plt.Figure()
ax1 = fig.add_subplot(111)
ax1.plot(axon.x, axon.y)
ax1.plot(axon.x[::internode_segments], axon.y[::internode_segments], 'x')
ax1.set_xlabel('x in µm')
ax1.set_ylabel('y in µm')
return fig
def plot_axon_nerve_shape_xy_coordinates(axon, nerve_shape):
fig = plt.Figure()
ax1 = fig.add_subplot(111)
ax1.plot(axon.x, axon.y)
ax1.plot(nerve_shape.x, nerve_shape.y)
ax1.set_xlabel('x in µm')
ax1.set_ylabel('y in µm')
return fig