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paper-fitting-and-validation-one-cell-zoom.py
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#!/usr/bin/env python2
from __future__ import print_function
import sys
sys.path.append('../lib')
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.path import Path
from matplotlib.patches import PathPatch
import string
import protocols
import model_ikr as m
from releakcorrect import I_releak, score_leak, protocol_leak_check
from scipy.optimize import fmin
# Set seed
np.random.seed(101)
savedir = './figs/paper'
if not os.path.isdir(savedir):
os.makedirs(savedir)
savedirlr = './figs/paper-low-res'
if not os.path.isdir(savedirlr):
os.makedirs(savedirlr)
cell = 'D19'
#
# Protocol info
#
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': protocols.sactiv,
'sinactiv': protocols.sinactiv,
}
protocol_dir = '../protocol-time-series'
protocol_list = [
'staircaseramp',
'sactiv',
'sinactiv',
'pharma',
'apab',
'apabv3',
'ap05hz',
'ap1hz',
'ap2hz',
]
# IV protocol special treatment
protocol_iv = [
'sactiv',
'sinactiv',
]
protocol_iv_times = {
'sactiv': protocols.sactiv_times,
'sinactiv': protocols.sinactiv_times,
}
protocol_iv_convert = {
'sactiv': protocols.sactiv_convert,
'sinactiv': protocols.sinactiv_convert,
}
protocol_iv_args = {
'sactiv': protocols.sactiv_iv_arg,
'sinactiv': protocols.sinactiv_iv_arg,
}
protocol_iv_v = {
'sactiv': protocols.sactiv_v,
'sinactiv': protocols.sinactiv_v,
}
data_dir_staircase = '../data'
data_dir = '../data-autoLC'
file_dir = './out'
file_list = [
'herg25oc1',
]
temperatures = np.array([25.0])
temperatures += 273.15 # in K
fit_seed = '542811797'
file_name = file_list[0]
temperature = temperatures[0]
#
# 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': [(0.0475, 0.0575), (0.32, 0.33)],
'apabv3': [(0.05, 0.07)],
'ap05hz': [(0.04, 0.07), (2.04, 2.07)],
'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,
}
#
# Do a very very tailored version........ :(
#
fig = plt.figure(figsize=(16, 15))
n_maxzoom = 7
bigxgap = 12
n_xgrid = n_maxzoom * 6 * 2
bigygap = 4
n_ygrid = 19
grid = plt.GridSpec(3 * n_ygrid + 2 * bigygap, 3 * n_xgrid + 2 * bigxgap,
hspace=0.0, wspace=0.0)
axes = np.empty([9, int(len(protocol_list) / 3)], dtype=object)
# long list here:
for i in range(int(len(protocol_list) / 3)):
i_grid = i * (n_xgrid + bigxgap)
f_grid = (i + 1) * n_xgrid + i * bigxgap
# First 'row'
if i == 0: # staircase-ramp
axes[0, i] = fig.add_subplot(grid[0:4, i_grid:f_grid])
axes[0, i].set_xticklabels([])
axes[1, i] = fig.add_subplot(grid[4:11, i_grid:f_grid])
axes[2, i] = np.empty(n_maxzoom, dtype=object) # grid[13:19, _])
else:
axes[0, i] = fig.add_subplot(grid[0:7, i_grid:f_grid])
axes[1, i] = fig.add_subplot(grid[10:19, i_grid:f_grid])
# Second 'row'
n_shift = n_ygrid + bigygap
axes[3, i] = fig.add_subplot(grid[n_shift+0:n_shift+4, i_grid:f_grid])
axes[3, i].set_xticklabels([])
axes[4, i] = fig.add_subplot(grid[n_shift+4:n_shift+11, i_grid:f_grid])
axes[5, i] = np.empty(n_maxzoom, dtype=object)
# Third 'row'
n_shift = 2 * (n_ygrid + bigygap)
axes[6, i] = fig.add_subplot(grid[n_shift+0:n_shift+4, i_grid:f_grid])
axes[6, i].set_xticklabels([])
axes[7, i] = fig.add_subplot(grid[n_shift+4:n_shift+11, i_grid:f_grid])
axes[8, i] = np.empty(n_maxzoom, dtype=object)
r_ngrid = {
2: (13, 19),
5: (n_ygrid + bigygap + 13, n_ygrid + bigygap + 19),
8: (2 * (n_ygrid + bigygap) + 13, 2 * (n_ygrid + bigygap) + 19),
}
# Do zoom in
# staircase-ramp specifal case
ai = 2
n_zoom = 6
assert(n_xgrid % n_zoom == 0)
cf = int(n_xgrid / n_zoom)
axes[ai, 0][0] = fig.add_subplot(grid[r_ngrid[ai][0]:r_ngrid[ai][1],
0:2*cf])
axes[ai, 0][1] = fig.add_subplot(grid[r_ngrid[ai][0]:r_ngrid[ai][1],
2*cf:3*cf])
axes[ai, 0][2] = fig.add_subplot(grid[r_ngrid[ai][0]:r_ngrid[ai][1],
3*cf:4*cf])
axes[ai, 0][3] = fig.add_subplot(grid[r_ngrid[ai][0]:r_ngrid[ai][1],
4*cf:6*cf])
for i in range(len(zoom_in_win['staircaseramp'])):
axes[ai, 0][i].set_xticklabels([])
axes[ai, 0][i].set_xticks([])
if i > 0:
axes[ai, 0][i].set_yticklabels([])
axes[ai, 0][i].set_yticks([])
# the rest
for i_prt, prt in enumerate(protocol_list):
ai, aj = 3 * int(i_prt / 3) + 2, i_prt % 3
if prt == 'staircaseramp' or (prt in protocol_iv):
continue
n_zoom = len(zoom_in_win[prt])
assert(n_xgrid % n_zoom == 0)
n = int(n_xgrid / n_zoom)
n_shift = aj * (n_xgrid + bigxgap)
for i in range(n_zoom):
axes[ai, aj][i] = fig.add_subplot(
grid[r_ngrid[ai][0]:r_ngrid[ai][1],
n_shift + i * n:n_shift + (i + 1) * n])
axes[ai, aj][i].set_xticklabels([])
axes[ai, aj][i].set_xticks([])
if i > 0:
axes[ai, aj][i].set_yticklabels([])
axes[ai, aj][i].set_yticks([])
# Set labels
axes[0, 0].set_ylabel('Voltage\n[mV]', fontsize=14)
axes[1, 0].set_ylabel('Current\n[pA]', fontsize=14)
axes[2, 0][0].set_ylabel('Zoom in', fontsize=14)
axes[3, 0].set_ylabel('Voltage\n[mV]', fontsize=14)
axes[4, 0].set_ylabel('Current\n[pA]', fontsize=14)
axes[5, 0][0].set_ylabel('Zoom in', fontsize=14)
axes[6, 0].set_ylabel('Voltage\n[mV]', fontsize=14)
axes[7, 0].set_ylabel('Current\n[pA]', fontsize=14)
axes[8, 0][0].set_ylabel('Zoom in', fontsize=14)
axes[2, 0][1].text(1, -0.3,
'Time [s]', fontsize=14, ha='center', va='center',
transform=axes[2, 0][1].transAxes)
axes[-4, 0][0].text(1, -0.3,
'Time [s]', fontsize=14, ha='center', va='center',
transform=axes[-4, 0][0].transAxes)
axes[-4, 1][0].text(1, -0.3,
'Time [s]', fontsize=14, ha='center', va='center',
transform=axes[-4, 1][0].transAxes)
axes[-4, 2][0].text(0.5, -0.3,
'Time [s]', fontsize=14, ha='center', va='center',
transform=axes[-4, 2][0].transAxes)
axes[-1, 0][0].text(1, -0.35,
'Time [s]', fontsize=18, ha='center', va='center',
transform=axes[-1, 0][0].transAxes)
axes[-1, 1][1].text(1, -0.35,
'Time [s]', fontsize=18, ha='center', va='center',
transform=axes[-1, 1][1].transAxes)
axes[-1, 2][3].text(0.5, -0.35,
'Time [s]', fontsize=18, ha='center', va='center',
transform=axes[-1, 2][3].transAxes)
# Add special x,y-label for IV protocols
axes[1, 1].set_xlabel('Voltage [mV]', fontsize=14)
axes[1, 2].set_xlabel('Voltage [mV]', fontsize=14)
# change y-label to right
for aj in [1, 2]:
axes[1, aj].yaxis.tick_right()
axes[1, aj].yaxis.set_label_position("right")
axes[0, aj].set_xlabel('Time [s]', fontsize=14)
axes[1, -1].set_ylabel('Normalised\ncurrent', fontsize=14)
axes[1, 1].set_ylim(-0.05, 1.05)
axes[1, 2].set_ylim(-5, 1.05)
# Set y-ticklabels for protocols
# TODO
#
# Protocol and a cell
#
# 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
# Fitted parameters
param_file = '%s/%s/%s-staircaseramp-%s-solution-%s.txt' % \
(file_dir, file_name, file_name, cell, fit_seed)
obtained_parameters = np.loadtxt(param_file)
for i_prt, prt in enumerate(protocol_list):
# Calculate axis index
ai, aj = 3 * int(i_prt / 3), i_prt % 3
# Title
if prt == 'staircaseramp':
axes[ai, aj].set_title('Calibration', fontsize=16, loc='left')
else:
axes[ai, aj].set_title('Validation %s' % i_prt, fontsize=16,
loc='left')
# Add label!
if prt not in protocol_iv:
axes[ai, aj].text(-0.1, 1.1, string.ascii_uppercase[i_prt],
transform=axes[ai, aj].transAxes, size=20,
weight='bold')
else:
axes[ai, aj].text(-0.1, 1.06, string.ascii_uppercase[i_prt],
transform=axes[ai, aj].transAxes, size=20,
weight='bold')
# Time point
times = np.loadtxt('%s/%s-%s-times.csv' % (data_dir, file_name,
prt), delimiter=',', skiprows=1)
# Protocol
model = prt2model[prt]
if prt not in protocol_iv:
times_sim = np.copy(times)
voltage = model.voltage(times_sim) * 1000
else:
times_sim = protocol_iv_times[prt](times[1] - times[0])
voltage = model.voltage(times_sim) * 1000
voltage, t = protocol_iv_convert[prt](voltage, times_sim)
assert(np.mean(np.abs(t - times)) < 1e-8)
axes[ai, aj].set_ylim((np.min(voltage) - 10, np.max(voltage) + 15))
# Data
if prt == 'staircaseramp':
data = np.loadtxt('%s/%s-%s-%s.csv' % (data_dir_staircase,
file_name, prt, cell), delimiter=',', skiprows=1)
elif prt not in protocol_iv:
data = np.loadtxt('%s/%s-%s-%s.csv' % (data_dir, file_name,
prt, cell), delimiter=',', skiprows=1)
# Re-leak correct the leak corrected data...
g_releak = fmin(score_leak, [0.0], args=(data, voltage, times,
protocol_leak_check[prt]), disp=False)
data = I_releak(g_releak[0], data, voltage)
else:
data = np.loadtxt('%s/%s-%s-%s.csv' % (data_dir, file_name,
prt, cell), delimiter=',', skiprows=1)
for i in range(data.shape[1]):
g_releak = fmin(score_leak, [0.0], args=(data[:, i],
voltage[:, i], times,
protocol_leak_check[prt]), disp=False)
data[:, i] = I_releak(g_releak[0], data[:, i], voltage[:, i])
assert(len(data) == len(times))
# Simulation
simulation = model.simulate(obtained_parameters, times_sim)
if prt in protocol_iv:
simulation, t = protocol_iv_convert[prt](simulation, times_sim)
assert(np.mean(np.abs(t - times)) < 1e-8)
# Plot
if prt not in protocol_iv:
# protocol
axes[ai, aj].plot(times, voltage, c='#696969')
# recording
axes[ai + 1, aj].plot(times, data, lw=1, alpha=0.8, c='#1f77b4',
label='data')
# simulation
if prt == 'staircaseramp':
axes[ai + 1, aj].plot(times, simulation, lw=2, c='#d62728',
label='model fit to data')
else:
axes[ai + 1, aj].plot(times, simulation, lw=2, c='#d62728',
label='model prediction')
else:
# protocol
for i in range(voltage.shape[1]):
axes[ai, aj].plot(times, voltage[:, i], c='#696969')
iv_v = protocol_iv_v[prt]() * 1000 # mV
# recording
iv_i = protocols.get_corrected_iv(data, times,
*protocol_iv_args[prt]())
axes[ai + 1, aj].plot(iv_v, iv_i / np.max(iv_i), lw=2, alpha=1,
c='#1f77b4', label='data')
# simulation
iv_i = protocols.get_corrected_iv(simulation, times,
*protocol_iv_args[prt]())
axes[ai + 1, aj].plot(iv_v, iv_i / np.max(iv_i), lw=2, alpha=1,
c='#d62728', label='model prediction')
# Plot zoom in version
if prt not in protocol_iv:
amplitude = np.max(simulation) - np.min(simulation)
for i_z, (t_i, t_f) in enumerate(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 -= 0.2 * y_amp
y_max += 0.2 * y_amp
y_amp = y_max - y_min
else:
y_min = np.min(simulation) - 0.2 * amplitude
y_max = np.max(simulation) + 0.2 * amplitude
# Fix ylim if we think we need it to be bigger for second panel
axes[ai + 1, aj].set_ylim(
(min(y_min, axes[ai + 1, aj].get_ylim()[0])),
(max(y_max, axes[ai + 1, aj].get_ylim()[1])))
# set specific ylim
if prt == 'ap1hz':
axes[ai + 1, aj].set_ylim([-40, 90])
y_min_t, y_max_t = -40, 90
elif prt == 'ap2hz':
axes[ai + 1, aj].set_ylim([-40, 180])
y_min_t, y_max_t = -40, 180
else:
y_min_t, y_max_t = y_min, y_max
# And plot shading over second panels
codes = [Path.MOVETO] + [Path.LINETO] * 3 + [Path.CLOSEPOLY]
vertices = np.array([(times[idx_i], y_min_t),
(times[idx_i], y_max_t),
(times[idx_f], y_max_t),
(times[idx_f], y_min_t),
(0, 0)], float)
pathpatch = PathPatch(Path(vertices, codes),
facecolor='#2ca02c',
edgecolor='#2ca02c',
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]
axes[ai + 2, aj][i_z].plot(times[idx_i:idx_f],
zoom_in_segment_data, lw=2, alpha=0.5, c='#1f77b4')
axes[ai + 2, aj][i_z].plot(times[idx_i:idx_f],
zoom_in_segment_sim, lw=2, c='#d62728')
axes[ai + 2, aj][i_z].set_xlim([times[idx_i], times[idx_f]])
if norm_zoom:
axes[ai + 2, aj][i_z].set_ylim([0, 1])
else:
axes[ai + 2, aj][i_z].set_ylim([y_min, y_max])
# Add trapezium over second and third panels
top_v = [(times[idx_i], y_min_t), (times[idx_f], y_min_t)]
bottom_v = axes[ai + 2, aj][i_z].transData.transform(
[(times[idx_f], y_max), (times[idx_i], y_max)])
inv = axes[ai + 1, aj].transData.inverted()
vertices = list(top_v) + list(inv.transform(bottom_v)) \
+ list([(0, 0)])
pathpatch = PathPatch(Path(vertices, codes),
facecolor='#2ca02c',
edgecolor='#2ca02c',
clip_on=False,
alpha=0.15)
plt.sca(axes[ai + 1, aj])
pyplot_axes = plt.gca()
pyplot_axes.add_patch(pathpatch)
# Set arrow and time duration
axes[ai + 2, aj][i_z].arrow(1, -0.05, -1, 0,
length_includes_head=True,
head_width=0.03, head_length=0.05, clip_on=False,
fc='k', ec='k', transform=axes[ai + 2, aj][i_z].transAxes)
axes[ai + 2, aj][i_z].arrow(0, -0.05, 1, 0,
length_includes_head=True,
head_width=0.03, head_length=0.05, clip_on=False,
fc='k', ec='k', transform=axes[ai + 2, aj][i_z].transAxes)
axes[ai + 2, aj][i_z].text(0.5, -0.15,
'%s' % np.around(t_f - t_i, decimals=3),
transform=axes[ai + 2, aj][i_z].transAxes,
horizontalalignment='center',
verticalalignment='center')
#
# Final adjustment and save
#
axes[1, 0].legend()
axes[1, 1].legend()
grid.tight_layout(fig, pad=0.6, rect=(0, 0.04, 1, 1))
grid.update(wspace=20, hspace=0.0)
plt.savefig('%s/fitting-and-validation-%s_%s-zoom.png' % (savedirlr, \
file_name, cell), bbox_inch='tight', pad_inches=0, dpi=100)
plt.savefig('%s/fitting-and-validation-%s_%s-zoom.png' % (savedir, file_name, \
cell), bbox_inch='tight', pad_inches=0, dpi=300)
plt.savefig('%s/fitting-and-validation-%s_%s-zoom.pdf' % (savedir, file_name, \
cell), format='pdf', bbox_inch='tight', pad_inches=0)
print('Done')