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paper-rmsd-hist.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)
refcell = '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',
]
validation_idx = [
None,
# 1,
# 2,
3,
4,
5,
# 6,
7,
8,
]
# 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]
# Load RMSD matrix
rmsd_matrix_file = './figs/rmsd-hist-%s-autoLC-releak/rmsd-matrix.txt' \
% file_name
rmsd_cells_file = './figs/rmsd-hist-%s-autoLC-releak/rmsd-matrix-cells.txt' \
% file_name
rmsd_matrix = np.loadtxt(rmsd_matrix_file)
with open(rmsd_matrix_file, 'r') as f:
rmsd_prt = f.readline().strip('\n').strip('#').split()
rmsd_cells = []
with open(rmsd_cells_file, 'r') as f:
for l in f:
if not l.startswith('#'):
rmsd_cells.append(l.strip('\n').split('-')[1])
rankedcolours = ['#1a9850',
'#fc8d59',
'#d73027',
'#7f7f7f']
rankedlabels = [r'$*$',
u'\u2021',
r'#',
u'\u2666']
#
# Do a very very tailored version........ :(
#
fig = plt.figure(figsize=(16, 15))
bigxgap = 12
n_xgrid = 84
bigygap = 5
n_ygrid = 31
grid = plt.GridSpec(2 * n_ygrid + 1 * bigygap, 3 * n_xgrid + 2 * bigxgap,
hspace=0.0, wspace=0.0)
axes = np.empty([10, int(len(protocol_list) / 2)], dtype=object)
# long list here:
for i in range(int(len(protocol_list) / 2)):
i_grid = i * (n_xgrid + bigxgap)
f_grid = (i + 1) * n_xgrid + i * bigxgap
# First 'row'
axes[0, i] = fig.add_subplot(grid[0:3, i_grid:f_grid])
axes[0, i].set_xticklabels([])
axes[1, i] = fig.add_subplot(grid[3:9, i_grid:f_grid])
axes[1, i].set_xticklabels([])
axes[2, i] = fig.add_subplot(grid[9:15, i_grid:f_grid])
axes[2, i].set_xticklabels([])
axes[3, i] = fig.add_subplot(grid[15:21, i_grid:f_grid])
# Histogram
axes[4, i] = fig.add_subplot(grid[24:31, i_grid:f_grid])
# Second 'row'
n_shift = n_ygrid + bigygap
axes[5, i] = fig.add_subplot(grid[n_shift+0:n_shift+3, i_grid:f_grid])
axes[5, i].set_xticklabels([])
axes[6, i] = fig.add_subplot(grid[n_shift+3:n_shift+9, i_grid:f_grid])
axes[6, i].set_xticklabels([])
axes[7, i] = fig.add_subplot(grid[n_shift+9:n_shift+15, i_grid:f_grid])
axes[7, i].set_xticklabels([])
axes[8, i] = fig.add_subplot(grid[n_shift+15:n_shift+21, i_grid:f_grid])
# Histogram
axes[9, i] = fig.add_subplot(grid[n_shift+24:n_shift+31, i_grid:f_grid])
# Set x-labels
axes[3, i].set_xlabel('Time [s]', fontsize=14)
axes[4, i].set_xlabel('RRMSE', fontsize=14)
axes[8, i].set_xlabel('Time [s]', fontsize=14)
axes[9, i].set_xlabel('RRMSE', fontsize=14)
# Set labels
axes[0, 0].set_ylabel('Voltage\n[mV]', fontsize=14)
axes[1, 0].set_ylabel(r'Best ($*$)', fontsize=14, color=rankedcolours[0])
axes[2, 0].set_ylabel(u'Median (\u2021)', fontsize=14, color=rankedcolours[1])
axes[3, 0].set_ylabel(r'90%ile (#)', fontsize=14, color=rankedcolours[2])
axes[4, 0].set_ylabel('Frequency\n[N=%s]' % len(rmsd_cells), fontsize=14)
axes[5, 0].set_ylabel('Voltage\n[mV]', fontsize=14)
axes[6, 0].set_ylabel(r'Best ($*$)', fontsize=14, color=rankedcolours[0])
axes[7, 0].set_ylabel(u'Median (\u2021)', fontsize=14, color=rankedcolours[1])
axes[8, 0].set_ylabel(r'90%ile (#)', fontsize=14, color=rankedcolours[2])
axes[9, 0].set_ylabel('Frequency\n[N=%s]' % len(rmsd_cells), fontsize=14)
axes[2, 0].text(-0.25, 0.5, 'Current [pA]', rotation=90, fontsize=18,
transform=axes[2, 0].transAxes, ha='center', va='center')
axes[7, 0].text(-0.25, 0.5, 'Current [pA]', rotation=90, fontsize=18,
transform=axes[7, 0].transAxes, ha='center', va='center')
# Set y-ticklabels for protocols
# TODO
#
# 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
#
# Plot
#
for i_prt, prt in enumerate(protocol_list):
# Calculate axis index
ai, aj = 5 * int(i_prt / 3), i_prt % 3
# Title
if prt == 'staircaseramp':
axes[ai, aj].set_title('Calibration', fontsize=16)
else:
axes[ai, aj].set_title('Validation %s' % validation_idx[i_prt],
fontsize=16)
# Add label!
axes[ai, aj].text(-0.1, 1.4, 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))
# Plot protocol
if prt not in protocol_iv:
axes[ai, aj].plot(times, voltage, c='#696969')
else:
# protocol
for i in range(voltage.shape[1]):
axes[ai, aj].plot(times, voltage[:, i], c='#696969')
# Calculate ranking
rmsd = rmsd_matrix[:, rmsd_prt.index(prt)]
best_cell = np.argmin(rmsd)
median_cell = np.argsort(rmsd)[len(rmsd)//2]
p90_cell = np.argsort(rmsd)[int(len(rmsd)*0.9)]
rankedcells = [rmsd_cells[best_cell],
rmsd_cells[median_cell],
rmsd_cells[p90_cell]]
rankedvalues = [rmsd[best_cell],
rmsd[median_cell],
rmsd[p90_cell],
rmsd[rmsd_cells.index(refcell)]]
for i_cell, cell in enumerate(rankedcells):
# 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))
# 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)
# 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)
# Work out ylim
maximum = np.percentile(simulation, 100)
minimum = np.percentile(simulation, 0.0)
amplitude = maximum - minimum
if prt in ['apabv3', 'ap05hz']:
maximum += 0.6 * amplitude
minimum -= 0.6 * amplitude
elif prt in ['apab', 'ap1hz']:
maximum += 0.3 * amplitude
minimum -= 0.3 * amplitude
else:
maximum += 0.15 * amplitude
minimum -= 0.15 * amplitude
axes[ai + i_cell + 1, aj].set_ylim([minimum, maximum])
# Plot
if prt not in protocol_iv:
# recording
axes[ai + i_cell + 1, aj].plot(times, data, lw=1, alpha=0.8,
c='#1f77b4', label='data')
# simulation
if prt == 'staircaseramp':
axes[ai + i_cell + 1, aj].plot(times, simulation, lw=2,
c='#d62728', label='model fit to data')
else:
axes[ai + i_cell + 1, aj].plot(times, simulation, lw=2,
c='#d62728', label='model prediction')
else:
iv_v = protocol_iv_v[prt]() * 1000 # V -> mV
# recording
iv_i = protocols.get_corrected_iv(data, times,
*protocol_iv_args[prt]())
axes[ai + i_cell + 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 + i_cell + 1, aj].plot(iv_v, iv_i / np.max(iv_i), lw=2,
alpha=1, c='#d62728', label='model prediction')
# Plot rmsd histogram
n, b, _ = axes[ai + 4, aj].hist(rmsd, bins=15, color='#9e9ac8')
# Add labels
rankedidx = []
for i, v in enumerate(rankedvalues):
idx = np.where(b <= v)[0][-1]
if idx in rankedidx:
print('Ref. marker might clash with other markers...')
axes[ai + 4, aj].text((b[idx] + b[idx + 1]) / 2., n[idx] + 3,
rankedlabels[i], fontsize=16, color=rankedcolours[i],
ha='center', va='center')
if n[idx] == np.max(n):
axes[ai + 4, aj].set_ylim([0, n[idx] + 6])
rankedidx.append(idx)
#
# Final adjustment and save
#
axes[1, 0].legend()
axes[1, 1].legend()
grid.tight_layout(fig, pad=0.6, rect=(0.02, 0.0, 1, 0.99))
grid.update(wspace=0.2, hspace=0.0)
plt.savefig('%s/rmsd-hist.png' % (savedir), bbox_inch='tight', pad_inches=0,
dpi=300)
plt.savefig('%s/rmsd-hist.pdf' % (savedir), format='pdf', bbox_inch='tight',
pad_inches=0)
print('Done')