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plot-validation-protocols-zoom.py
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#!/usr/bin/env python2
import sys
sys.path.append('../lib')
import os
import numpy as np
if '--show' not in sys.argv:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch
import glob
import protocols
import model_ikr as m
savepath = './figs'
if not os.path.isdir(savepath):
os.makedirs(savepath)
#
# Protocols
#
protocol_funcs = {
'staircaseramp': protocols.leak_staircase,
'pharma': protocols.pharma, # during drug application
'apab': 'protocol-apab.csv',
'apabv3': 'protocol-apabv3.csv',
'ap05hz': 'protocol-ap05hz.csv',
'ap1hz': 'protocol-ap1hz.csv',
'ap2hz': 'protocol-ap2hz.csv',
'sactiv': None,
'sinactiv': None,
}
protocol_dir = '../protocol-time-series'
protocol_list = [
'staircaseramp',
'pharma',
'apab',
'apabv3',
'ap1hz',
'ap2hz',
]
data_dir = '../data-autoLC'
data_dir_staircase = '../data'
file_dir = './out'
file_list = [
'herg25oc1',
]
temperatures = np.array([25.0])
temperatures += 273.15 # in K
fit_seed = '542811797'
withfcap = False
#
# Where to zoom in
#
norm_zoom = False
zoom_in_win = { # protocol: [(time_start, time_end), ...] in second
# 'staircaseramp': [(1.8, 2.5), (11.395, 11.415), (13.895, 13.915),
# (14.375, 14.925)],
'staircaseramp': [(1.875, 2.125), (11.35, 11.45), (13.85, 13.95),
(14.375, 14.625)],
'pharma': [(0.64, 0.66), (1.14, 1.16)],
'apab': [(0.035, 0.065), (0.32, 0.33)],
'apabv3': [(0.05, 0.07)],
'ap05hz': None,
'ap1hz': [(0.04, 0.07), (1.04, 1.07),
(2.04, 2.07), (3.04, 3.07)],
'ap2hz': [(0.045, 0.06), (0.545, 0.56),
(1.045, 1.06), (1.545, 1.56),
(2.045, 2.06), (2.545, 2.56),
(3.045, 3.06)],
'sactiv': None,
'sinactiv': None,
}
#
# Get new parameters and traces
#
for i_temperature, (file_name, temperature) in enumerate(zip(file_list,
temperatures)):
print('Plotting %s' % file_name)
savedir = '%s/%s-autoLC-zoom' % (savepath, file_name)
if not os.path.isdir(savedir):
os.makedirs(savedir)
# Get selected cells
files_dir = os.path.realpath(os.path.join(file_dir, file_name))
searchwfcap = '-fcap' if withfcap else ''
selectedfile = './manualselected-%s.txt' % (file_name)
selectedwell = []
with open(selectedfile, 'r') as f:
for l in f:
if not l.startswith('#'):
selectedwell.append(l.split()[0])
# Model
prt2model = {}
for prt in protocol_list:
protocol_def = protocol_funcs[prt]
if type(protocol_def) is str:
protocol_def = '%s/%s' % (protocol_dir, protocol_def)
prt2model[prt] = m.Model('../mmt-model-files/kylie-2017-IKr.mmt',
protocol_def=protocol_def,
temperature=temperature, # K
transform=None,
useFilterCap=False) # ignore capacitive spike
for cell in selectedwell[:]:
# Fitted parameters
param_file = '%s/%s-staircaseramp-%s-solution%s-%s.txt' % \
(files_dir, file_name, cell, searchwfcap, fit_seed)
obtained_parameters = np.loadtxt(param_file)
# Create figure
# Do a very very tailored version........ :(
fig = plt.figure(figsize=(16, 12))
grid = plt.GridSpec(40, 3, hspace=0.0, wspace=0.2)
axes = np.empty([6, int(len(protocol_list)/2)], dtype=object)
# long list here:
for i in range(int(len(protocol_list)/2)):
# First 'row'
axes[0, i] = fig.add_subplot(grid[0:5, i]) # , sharex=axes[2, i])
axes[0, i].set_xticklabels([])
axes[1, i] = fig.add_subplot(grid[5:10, i]) # , sharex=axes[2, i])
axes[2, i] = fig.add_subplot(grid[13:18, i])
axes[2, i].set_xticklabels([]) # last one is zoom in
# Second 'row'
axes[3, i] = fig.add_subplot(grid[22:27, i]) # , sharex=axes[5, i])
axes[3, i].set_xticklabels([])
axes[4, i] = fig.add_subplot(grid[27:32, i]) # , sharex=axes[5, i])
axes[5, i] = fig.add_subplot(grid[35:40, i])
axes[5, i].set_xticklabels([]) # last one is zoom in
if norm_zoom:
axes[2, i].set_yticklabels([])
axes[5, i].set_yticklabels([])
# Set labels
axes[0, 0].set_ylabel('Voltage [mV]', fontsize=14)
axes[1, 0].set_ylabel('Current [pA]', fontsize=14)
axes[2, 0].set_ylabel('Zoom in', fontsize=14)
axes[3, 0].set_ylabel('Voltage [mV]', fontsize=14)
axes[4, 0].set_ylabel('Current [pA]', fontsize=14)
axes[5, 0].set_ylabel('Zoom in', fontsize=14)
axes[-1, len(protocol_list) // 2 // 2].set_xlabel('Time [s]',
fontsize=18)
for i_prt, prt in enumerate(protocol_list):
# Data
if prt == 'staircaseramp':
data = np.loadtxt('%s/%s-%s-%s.csv' % (data_dir_staircase,
file_name, prt, cell), delimiter=',', skiprows=1)
else:
data = np.loadtxt('%s/%s-%s-%s.csv' % (data_dir, file_name,
prt, cell), delimiter=',', skiprows=1)
times = np.loadtxt('%s/%s-%s-times.csv' % (data_dir, file_name,
prt), delimiter=',', skiprows=1)
assert(data.shape == times.shape)
# Simulation
model = prt2model[prt]
simulation = model.simulate(obtained_parameters, times)
if False:
for _ in range(5):
assert(all(simulation ==
model.simulate(obtained_parameters, times)))
voltage = model.voltage(times)
# Plot
ai = (i_prt // (len(protocol_list) // 2)) * 3
aj = i_prt % (len(protocol_list) // 2)
amplitude = np.max(simulation) - np.min(simulation)
if prt == 'staircaseramp':
axes[ai, aj].set_title('Calibration', fontsize=16)
# Fix ylim using simulation
axes[ai + 1, aj].set_ylim([
np.min(simulation) - 0.05 * amplitude,
np.max(simulation) + 0.05 * amplitude])
else:
axes[ai, aj].set_title('Validation %s' % i_prt, fontsize=16)
# Fix ylim using simulation
axes[ai + 1, aj].set_ylim([
np.min(simulation) - 0.3 * amplitude,
np.max(simulation) + 0.3 * amplitude])
axes[ai, aj].plot(times, voltage)
axes[ai + 1, aj].plot(times, data, alpha=0.5, label='Data')
axes[ai + 1, aj].plot(times, simulation, label='Model')
# Plot zoom in version
zoom_in_data = []
zoom_in_simulation = []
zoom_in_line_break = []
for t_i, t_f in zoom_in_win[prt]:
# Find closest time
idx_i = np.argmin(np.abs(times - t_i))
idx_f = np.argmin(np.abs(times - t_f))
# Work out the max and min
if norm_zoom:
y_min = np.min(simulation[idx_i:idx_f])
y_max = np.max(simulation[idx_i:idx_f])
y_amp = y_max - y_min
y_min -= 0.3 * y_amp
y_max += 0.3 * y_amp
y_amp = y_max - y_min
else:
y_min = np.min(simulation) - 0.25 * amplitude
y_max = np.max(simulation) + 0.25 * amplitude
# And plot gray boxes over second panels
codes = [Path.MOVETO] + [Path.LINETO] * 3 + [Path.CLOSEPOLY]
vertices = np.array([(times[idx_i], y_min),
(times[idx_i], y_max),
(times[idx_f], y_max),
(times[idx_f], y_min),
(0, 0)], float)
pathpatch = PathPatch(Path(vertices, codes),
facecolor='#fa9fb5',
edgecolor='#fa9fb5',
alpha=0.75)
plt.sca(axes[ai + 1, aj])
pyplot_axes = plt.gca()
pyplot_axes.add_patch(pathpatch)
# Work out third panel plot
if norm_zoom:
zoom_in_segment_data = (data[idx_i:idx_f] - y_min) / y_amp
zoom_in_segment_sim = (simulation[idx_i:idx_f] - y_min) \
/ y_amp
else:
zoom_in_segment_data = data[idx_i:idx_f]
zoom_in_segment_sim = simulation[idx_i:idx_f]
zoom_in_data = np.append(zoom_in_data, zoom_in_segment_data)
zoom_in_simulation = np.append(zoom_in_simulation,
zoom_in_segment_sim)
zoom_in_line_break.append(len(zoom_in_segment_sim))
axes[ai + 2, aj].plot(zoom_in_data, alpha=0.5)
axes[ai + 2, aj].plot(zoom_in_simulation)
for x in np.cumsum(zoom_in_line_break)[:-1]:
axes[ai + 2, aj].axvline(x, color='k')
axes[ai + 2, aj].set_xlim([0, len(zoom_in_simulation)])
if norm_zoom:
axes[ai + 2, aj].set_ylim([0, 1])
else:
axes[ai + 2, aj].set_ylim([y_min, y_max])
axes[1, 0].legend()
grid.tight_layout(fig, pad=0.6)
grid.update(wspace=0.12, hspace=0.0)
if '--show' not in sys.argv:
plt.savefig('%s/%s.png' % (savedir, cell),
bbox_inches='tight', pad_inches=0)
else:
plt.show()
plt.close('all')
print('Done ' + file_name + cell)
del(prt2model)