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paper-fitting-and-validation-eyring-q102-v2.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
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
import string
import seaborn as sns
import protocols
from protocols import est_g_staircase
import model_ikr as m
from releakcorrect import I_releak, score_leak, protocol_leak_check
from scipy.optimize import fmin
# Set parameter transformation
import parametertransform
transform_to_model_param = parametertransform.log_transform_to_model_param
transform_from_model_param = parametertransform.log_transform_from_model_param
try:
prt = sys.argv[1]
except IndexError:
print('Usage: python %s [protocol]' % __file__)
sys.exit()
# 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)
# Colours for fan chart
fan_blue = ['#b5c7d5',
'#adc1d0',
'#91abbc',
'#85a0b1',
'#6b8fa9',
'#62869f',
'#587c96',
'#477390',
'#3f6c88',
]
#
# 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'
# 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 = [
'herg25oc',
'herg27oc',
'herg30oc',
'herg33oc',
'herg37oc',
]
file_list_tmp = [ # TODO
'herg25oc1',
'herg27oc1',
'herg30oc1',
'herg33oc1',
'herg37oc3',
]
temperatures = np.array([25.0, 27.0, 30.0, 33.0, 37.0])
temperatures += 273.15 # in K
fit_seed = '542811797'
# Load pseudo2hbm
mean_chains = []
for i_temperature, (file_name, temperature) in enumerate(zip(file_list,
temperatures)):
load_file = './out-mcmc/%s-pseudo2hbm-lognorm-mean.txt' % (file_name)
mean_chain = np.loadtxt(load_file) # transformed
mean_chains.append(mean_chain)
mean_chains = np.asarray(mean_chains)
# Eyring and Q10
from temperature_models import eyringA, eyringB, eyringG, eyringT
from temperature_models import q10A, q10B, q10G, q10T
from temperature_models import eyring_transform_to_model_param
eyring_mean = np.loadtxt('%s/eyring-mean.txt' % file_dir)
q10_mean = np.loadtxt('%s/q10-mean.txt' % file_dir)
q10_2mean = np.copy(q10_mean)
hbm_T_mean_q10 = transform_to_model_param(
np.mean(mean_chains[-1], axis=0))
for i in [2, 4, 6, 8]:
q10_2mean[i, 0] = hbm_T_mean_q10[i]
#
# Where to zoom in
#
norm_zoom = False
zoom_in_win = { # protocol: [[(time_start, time_end), ...] in second,
# [(grid_start, grid_end), ...] total 16]
# 'staircaseramp': [(1.8, 2.5), (11.395, 11.415), (13.895, 13.915),
# (14.375, 14.925)],
'staircaseramp': [[(1.875, 2.125),
# (4.25, 6.5),
(11.375, 11.425), (13.875, 13.925),
# (11.75, 13.25),
(14.375, 14.625)],
[(0, 5), (5, 8), (8, 11), (11, 16)]],
# [(0, 3), (3, 7), (7, 8), (8, 9), (9, 13), (13, 16)]],
'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)],
[(0, 8), (8, 16)]],
'ap1hz': [[(0.04, 0.07), (1.04, 1.07),
(2.04, 2.07), (3.04, 3.07)],
[(0, 4), (4, 8), (8, 12), (12, 16)]],
'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)],
[(0, 4), (4, 8), (8, 12), (12, 16)]],
'sactiv': None,
'sinactiv': None,
}
isNorm = True
norm_method = 1
save_norm_factor = False
zoom_colour = [
'#ffffcc',
'#e5d8bd',
'#fddaec',
'#fed9a6',
'#bdbdbd',
#
'#ffffcc',
'#e5d8bd',
'#fddaec',
'#fed9a6',
'#bdbdbd',
#
'#fbb4ae',
'#b3cde3',
'#ccebc5',
'#decbe4',
]
#
# Do a very very tailored version........ :(
#
fig = plt.figure(figsize=(11, 7))
n_maxzoom = 7
bigygap = 3
n_ygrid = 5 * 6
n_subpanels = 5
n_xgrid_1 = 24
n_xgrid_2 = 16
smallxgap = 2 # gap before zoom-in
n_xgrid = n_xgrid_1 + n_xgrid_2 + smallxgap
bigxgap = 4 # gap between columns
grid = plt.GridSpec(n_ygrid + 6,
n_xgrid, hspace=0.0, wspace=0.2)
axes = np.empty([n_subpanels + 1, 2], dtype=object)
# long list here:
for j in [0]:
# 0 row for protocol
jj = 2 * j
j_shift = n_xgrid + bigxgap
axes[0, jj] = fig.add_subplot(grid[0:4, j*j_shift:j*j_shift+n_xgrid_1])
axes[0, jj].set_xticklabels([])
for i in range(1):
# i 'big row'
n_shift = i * (n_ygrid + bigygap) + 6 # 6 is for protocol + gap
n_panel = n_subpanels * i
for j in range(1):
# j 'big column'
j_shift = n_xgrid + bigxgap
for k in range(5):
# k 'sub row'
ai, aj = n_panel + k + 1, 2 * j
# first 'sub column'
axes[ai, aj] = fig.add_subplot(
grid[n_shift + 6 * k:n_shift + 6 * (k + 1),
j * j_shift:j * j_shift + n_xgrid_1])
if k != 4:
axes[ai, aj].set_xticklabels([])
# second 'sub column'
axes[ai, aj + 1] = np.empty(n_maxzoom, dtype=object)
n_zoom = len(zoom_in_win[prt][1])
for l in range(n_zoom):
j_grid_start = j * j_shift + n_xgrid_1 + smallxgap \
+ zoom_in_win[prt][1][l][0]
j_grid_end = j * j_shift + n_xgrid_1 + smallxgap \
+ zoom_in_win[prt][1][l][1]
axes[ai, aj + 1][l] = fig.add_subplot(
grid[n_shift + 6 * k:n_shift + 6 * (k + 1),
j_grid_start:j_grid_end])
axes[ai, aj + 1][l].set_xticklabels([])
axes[ai, aj + 1][l].set_xticks([])
if l != 0:
axes[ai, aj + 1][l].set_yticklabels([])
axes[ai, aj + 1][l].set_yticks([])
# Labels
axes[0, 0].text(-0.125, 0.5, 'Voltage\n[mV]', fontsize=14,
rotation=90, ha='center', va='center',
transform=axes[0, 0].transAxes)
Ts_oC = temperatures - 273.15 # in oC
for k in range(1, 6):
T_oC = int(temperatures[k - 1] - 273.15)
axes[k, 0].set_ylabel('$%d\pm1^\circ$C' % T_oC, fontsize=12)
axes[3, 0].text(-0.15, 0.5, 'Normalised currents', fontsize=16,
rotation=90, ha='center', va='center',
transform=axes[3, 0].transAxes)
axes[-1, 0].set_xlabel('Time [s]', fontsize=16)
# Liudmila suggested common y-axis
n_zoom = len(zoom_in_win[prt][1])
for i in range(1):
ai = i * n_subpanels + 1
for j in range(1):
aj = 2 * j
if (i * 2 + j + 1) > len(temperatures):
continue
if prt == 'staircaseramp':
for ii in range(5):
axes[ai + ii, aj].set_ylim((-0.1, 1.25))
for l in range(n_zoom):
axes[ai + ii, aj + 1][l].set_ylim((-1.3, 1.1))
else:
for ii in range(5):
axes[ai + ii, aj].set_ylim((-0.1, 1.1))
for l in range(n_zoom):
axes[ai + ii, aj + 1][l].set_ylim((-0.1, 1.95))
# Add inset
if prt == 'staircaseramp':
ai, aj = -1, 0
axins = inset_axes(axes[ai, aj], 1.15, 0.5, loc=2)
axins.set_facecolor("#f1f1f1")
axins.set_xlim((4.25, 6.5))
axins.set_ylim((0.0, 0.6))
axins.set_xticks([])
axins.set_yticks([])
mark_inset(axes[ai, aj], axins, loc1=3, loc2=1, fc="#f1f1f1", ec="0.5")
axins2 = inset_axes(axes[ai, aj], 0.85, 0.4, loc=1)
axins2.set_facecolor("#f1f1f1")
axins2.set_xlim((11.75, 13.05))
axins2.set_ylim((0.2, 0.6))
axins2.set_xticks([])
axins2.set_yticks([])
mark_inset(axes[ai, aj], axins2, loc1=2, loc2=4, fc="#f1f1f1", ec="0.5")
#
# Plot!
#
if norm_method == 3:
if prt == 'staircaseramp':
norm_sim_all = []
norm_eyring_all = []
norm_q10_all = []
else:
try:
norm_sim_all = np.loadtxt('./out/norm-factors/hbm.txt',
skiprows=1).T
norm_eyring_all = np.loadtxt('./out/norm-factors/eyring.txt',
skiprows=1).T
norm_q10_all = np.loadtxt('./out/norm-factors/q10.txt',
skiprows=1).T
except IOError:
raise IOError('Expect running for prt=staircaseramp first')
for i_T, (file_name, T) in enumerate(zip(file_list_tmp, temperatures)):
# Model
protocol_def = protocol_funcs[prt]
if type(protocol_def) is str:
protocol_def = '%s/%s' % (protocol_dir, protocol_def)
model = m.Model('../mmt-model-files/kylie-2017-IKr.mmt',
protocol_def=protocol_def,
temperature=temperatures[0], # K
transform=None,
useFilterCap=False) # ignore capacitive spike
# Calculate axis index
ai, aj = i_T + 1, 0
# Time point
if prt == 'staircaseramp':
times = np.loadtxt('%s/%s-%s-times.csv' % (data_dir_staircase,
file_list[0] + '1', prt), delimiter=',', skiprows=1) # TODO
else:
times = np.loadtxt('%s/%s-%s-times.csv' % (data_dir, file_list[0] + '1',
prt), delimiter=',', skiprows=1)
# Protocol
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)
# Plot protocol
if i_T == 0:
if prt not in protocol_iv:
axes[ai - 1, aj].plot(times, voltage, c='#696969')
else:
for i in range(voltage.shape[1]):
axes[ai - 1, aj].plot(times, voltage[:, i], c='#696969')
axes[ai - 1, aj].set_ylim((np.min(voltage) - 10, np.max(voltage) + 15))
# Draw boxes over main plot panels
minimum = -200
maximum = 100 # mV, for plotting only, should be OK
for i_z, (t_i, t_f) in enumerate(zoom_in_win[prt][0]):
# Find closest time
idx_i = np.argmin(np.abs(times - t_i))
idx_f = np.argmin(np.abs(times - t_f))
codes = [Path.MOVETO] + [Path.LINETO] * 3 + [Path.CLOSEPOLY]
vertices = np.array([(times[idx_i], minimum),
(times[idx_i], maximum),
(times[idx_f], maximum),
(times[idx_f], minimum),
(0, 0)], float)
pathpatch = PathPatch(Path(vertices, codes),
facecolor=zoom_colour[i_z],
edgecolor=zoom_colour[i_z],
# linewidth=0,
alpha=0.75)
plt.sca(axes[ai - 1, aj])
pyplot_axes = plt.gca()
pyplot_axes.add_patch(pathpatch)
# Plot data as background
data_fancharts_dir = './out/data-fancharts'
percentiles = np.loadtxt('%s/percentiles.txt' % data_fancharts_dir)
fan_chart_data_top = np.loadtxt('%s/%s-%s-top.txt' % \
(data_fancharts_dir, file_name, prt))
fan_chart_data_bot = np.loadtxt('%s/%s-%s-bot.txt' % \
(data_fancharts_dir, file_name, prt))
if prt not in protocol_iv:
fan_x = np.loadtxt('%s/%s-%s-times.txt' % \
(data_fancharts_dir, file_name, prt))
else:
fan_x = np.loadtxt('%s/%s-%s-voltage.txt' % \
(data_fancharts_dir, file_name, prt))
for ii in range(1):
for i_p, p in enumerate(percentiles):
alpha = 0.8
color = fan_blue[i_p]
top = fan_chart_data_top[:, i_p]
bot = fan_chart_data_bot[:, i_p]
axes[ai + ii, aj].fill_between(fan_x, top, bot, color=color,
alpha=alpha, linewidth=0,
label='__nolegend__' if i_p else 'Data fan charts')
# Models
# HBM mean parameters
hbm_T_mean = transform_to_model_param(
np.mean(mean_chains[i_T], axis=0))
simulation = model.simulate(hbm_T_mean, times_sim)
# Eyring parameters
eyring_T_mean = eyringT(eyring_mean, T)
eyring_model_param = eyring_transform_to_model_param(eyring_T_mean, T)
eyring_sim = model.simulate(eyring_model_param, times_sim)
# Q10 parameters
q10_T_2mean = q10T(q10_2mean, T)
q10_2model_param = eyring_transform_to_model_param(q10_T_2mean, T)
q10_2sim = model.simulate(q10_2model_param, times_sim)
if norm_method == 1:
# Kylie's method, use a reference trace
# (should give the most similar plots)
top = fan_chart_data_top[:, -1]
bot = fan_chart_data_bot[:, -1]
ref_data = (top + bot) / 2. # TODO
from scipy.optimize import minimize
res_s = minimize(lambda x: np.sum(
np.abs(simulation / x - ref_data)),
x0=np.abs(np.min(simulation)))
norm_sim = res_s.x[0] if isNorm else 1.
res_e = minimize(lambda x: np.sum(
np.abs(eyring_sim / x - ref_data)), x0=norm_sim)
norm_eyring = res_e.x[0] if isNorm else 1.
res_q = minimize(lambda x: np.sum(
np.abs(q10_2sim / x - ref_data)), x0=norm_sim)
norm_q10 = res_q.x[0] if isNorm else 1.
if (norm_sim > 5e3 or not np.isfinite(norm_sim)):
# Simulation went wrong?!
raise RuntimeError('Simulation for HBM %s %s seems' % \
(file_name, prt) + ' problematic')
if (norm_eyring > 5e3 or not np.isfinite(norm_eyring)):
# Simulation went wrong?!
raise RuntimeError('Simulation for Eyring %s %s seems' % \
(file_name, prt) + ' problematic')
if (norm_q10 > 5e3 or not np.isfinite(norm_q10)):
# Simulation went wrong?!
raise RuntimeError('Simulation for Q10 %s %s seems' % \
(file_name, prt) + ' problematic')
elif norm_method == 3:
if prt == 'staircaseramp':
norm_sim = est_g_staircase(simulation, times_sim,
p0=[800, 0.025], debug=False)
norm_sim_all.append(norm_sim)
else:
norm_sim = norm_sim_all[i_T]
if prt == 'staircaseramp':
norm_eyring = est_g_staircase(eyring_sim, times_sim,
p0=[800, 0.025], debug=False)
norm_eyring_all.append(norm_eyring)
else:
norm_eyring = norm_eyring_all[i_T]
if prt == 'staircaseramp':
norm_q10 = est_g_staircase(q10_2sim, times_sim, p0=[800, 0.025],
debug=False)
norm_q10_all.append(norm_q10)
else:
norm_q10 = norm_q10_all[i_T]
elif norm_method == 4:
norm_sim = hbm_T_mean[0]
norm_eyring = eyring_model_param[0]
norm_q10 = q10_model_param[0]
# Mean individual cells fit
if prt in protocol_iv:
simulation, t = protocol_iv_convert[prt](simulation, times_sim)
assert(np.mean(np.abs(t - times)) < 1e-8)
iv_v = protocol_iv_v[prt]() * 1000 # mV
iv_i = protocols.get_corrected_iv(simulation, times,
*protocol_iv_args[prt]())
axes[ai, aj].plot(iv_v, iv_i / np.max(iv_i), lw=1, alpha=1,
ls='-', c='C1', zorder=1,
label='simplied pseudo-HBM mean')
else:
axes[ai, aj].plot(times_sim, simulation / norm_sim, alpha=1, lw=1,
ls='-', c='C1', zorder=1, label='simplied pseudo-HBM mean')
# Eyring
if prt in protocol_iv:
eyring_sim, t = protocol_iv_convert[prt](eyring_sim, times_sim)
assert(np.mean(np.abs(t - times)) < 1e-8)
iv_v = protocol_iv_v[prt]() * 1000 # mV
iv_i = protocols.get_corrected_iv(eyring_sim, times,
*protocol_iv_args[prt]())
axes[ai, aj].plot(iv_v, iv_i / np.max(iv_i), lw=1, alpha=1,
ls=':', c='C2', zorder=2, label='Eyring mean')
else:
axes[ai, aj].plot(times_sim,
eyring_sim / norm_eyring,
alpha=1, lw=1, ls=':', c='C2', zorder=2,
label='Eyring mean')
# Q10
if prt in protocol_iv:
q10_2sim, t = protocol_iv_convert[prt](q10_2sim, times_sim)
assert(np.mean(np.abs(t - times)) < 1e-8)
iv_v = protocol_iv_v[prt]() * 1000 # mV
iv_i = protocols.get_corrected_iv(q10_2sim, times,
*protocol_iv_args[prt]())
axes[ai, aj].plot(iv_v, iv_i / np.max(iv_i), lw=1, alpha=1,
ls='--', c='C3', zorder=3, label=r'Q$_{10}$ mean')
else:
axes[ai, aj].plot(times_sim,
q10_2sim / norm_q10, ls='--',
alpha=1, lw=1, c='C3', zorder=3, label=r'Q$_{10}$ mean')
# Zoom in
if prt not in protocol_iv:
for i_z, (t_i, t_f) in enumerate(zoom_in_win[prt][0]):
# Data fan chart
# Find closest time
idx_fi = np.argmin(np.abs(fan_x - t_i))
idx_ff = np.argmin(np.abs(fan_x - t_f))
# Segment and Plot
for ii in range(1):
for i_p, p in enumerate(percentiles):
alpha = 0.8
color = fan_blue[i_p]
top = fan_chart_data_top[:, i_p]
bot = fan_chart_data_bot[:, i_p]
zoom_in_segment_top = bot[idx_fi:idx_ff]
zoom_in_segment_bot = top[idx_fi:idx_ff]
axes[ai + ii, aj + 1][i_z].fill_between(
fan_x[idx_fi:idx_ff],
zoom_in_segment_top, zoom_in_segment_bot,
color=color, alpha=alpha, linewidth=0)
axes[ai + ii, aj + 1][i_z].set_xlim(
[fan_x[idx_fi], fan_x[idx_ff]])
# Models
# Find closest time
idx_i = np.argmin(np.abs(times_sim - t_i))
idx_f = np.argmin(np.abs(times_sim - t_f))
# Segments
zoom_in_segment_sim = simulation[idx_i:idx_f]
zoom_in_segment_eyring = eyring_sim[idx_i:idx_f]
zoom_in_segment_q10 = q10_2sim[idx_i:idx_f]
# Plot
axes[ai, aj + 1][i_z].plot(times_sim[idx_i:idx_f],
zoom_in_segment_sim / norm_sim, ls='-',
alpha=1, lw=1, c='C1', zorder=1)
axes[ai, aj + 1][i_z].plot(times_sim[idx_i:idx_f],
zoom_in_segment_eyring / norm_eyring, ls=':',
alpha=1, lw=1, c='C2', zorder=2)
axes[ai, aj + 1][i_z].plot(times_sim[idx_i:idx_f],
zoom_in_segment_q10 / norm_q10, ls='--',
alpha=1, lw=1, c='C3', zorder=3)
for ii in range(1):
axes[ai + ii, aj + 1][i_z].set_xlim(
[times_sim[idx_i], times_sim[idx_f]])
# Plot inset
if (prt == 'staircaseramp') and (i_T == len(temperatures) - 1):
for i_p, p in enumerate(percentiles):
alpha = 0.8
color = fan_blue[i_p]
top = fan_chart_data_top[:, i_p]
bot = fan_chart_data_bot[:, i_p]
axins.fill_between(fan_x, top, bot,
color=color, alpha=alpha, linewidth=0)
axins2.fill_between(fan_x, top, bot,
color=color, alpha=alpha, linewidth=0)
axins.plot(times_sim, simulation / norm_sim, ls='-',
alpha=1, lw=1, c='C1', zorder=1)
axins.plot(times_sim, eyring_sim / norm_eyring, ls=':',
alpha=1, lw=1, c='C2', zorder=2)
axins.plot(times_sim, q10_2sim / norm_q10, ls='--',
alpha=1, lw=1, c='C3', zorder=3)
axins2.plot(times_sim, simulation / norm_sim, ls='-',
alpha=1, lw=1, c='C1', zorder=1)
axins2.plot(times_sim, eyring_sim / norm_eyring, ls=':',
alpha=1, lw=1, c='C2', zorder=2)
axins2.plot(times_sim, q10_2sim / norm_q10, ls='--',
alpha=1, lw=1, c='C3', zorder=3)
# Draw zoom-in boxes
if prt not in protocol_iv:
if prt == 'staircaseramp':
minimum = -1.
maximum = 1.5
else:
minimum = -0.25
maximum = 1.5
for i_z, (t_i, t_f) in enumerate(zoom_in_win[prt][0]):
# Find closest time
idx_i = np.argmin(np.abs(times - t_i))
idx_f = np.argmin(np.abs(times - t_f))
# And plot gray boxes over main plot panels
codes = [Path.MOVETO] + [Path.LINETO] * 3 + [Path.CLOSEPOLY]
vertices = np.array([(times[idx_i], minimum),
(times[idx_i], maximum),
(times[idx_f], maximum),
(times[idx_f], minimum),
(0, 0)], float)
for ii in range(1):
pathpatch = PathPatch(Path(vertices, codes),
facecolor=zoom_colour[i_z],
edgecolor=zoom_colour[i_z],
# linewidth=0,
alpha=0.75)
plt.sca(axes[ai + ii, aj])
pyplot_axes = plt.gca()
pyplot_axes.add_patch(pathpatch)
# Set zoom-in panel colour
axes[ai, aj + 1][i_z].patch.set_facecolor(zoom_colour[i_z])
axes[ai, aj + 1][i_z].patch.set_alpha(0.5)
# Set arrow and time duration
if i_T == len(temperatures) - 1:
axes[ai, aj + 1][i_z].arrow(1, -0.075, -1, 0,
length_includes_head=True,
head_width=0.03, head_length=0.05, clip_on=False,
fc='k', ec='k',
transform=axes[ai, aj + 1][i_z].transAxes)
axes[ai, aj + 1][i_z].arrow(0, -0.075, 1, 0,
length_includes_head=True,
head_width=0.03, head_length=0.05, clip_on=False,
fc='k', ec='k',
transform=axes[ai, aj + 1][i_z].transAxes)
axes[ai, aj + 1][i_z].text(0.5, -0.2,
'%s' % np.around(t_f - t_i, decimals=3),
transform=axes[ai, aj + 1][i_z].transAxes,
horizontalalignment='center',
verticalalignment='center')
# Save norm factors
if prt == 'staircaseramp' and save_norm_factor:
def boolean_indexing(v, fillval=np.nan):
lens = np.array([len(item) for item in v])
mask = lens[:,None] > np.arange(lens.max())
out = np.full(mask.shape,fillval)
out[mask] = np.concatenate(v)
return out
header = 'Order follows `../../../manualselection/paper-rank-*` columns' \
+ ' are %s' % (' '.join(file_list))
if not os.path.isdir('./out/norm-factors'):
os.makedirs('./out/norm-factors')
if norm_method == 3:
np.savetxt('./out/norm-factors/hbm.txt',
boolean_indexing(norm_sim_all).T, header=header)
np.savetxt('./out/norm-factors/eyring.txt',
boolean_indexing(norm_eyring_all).T, header=header)
np.savetxt('./out/norm-factors/q10.txt',
boolean_indexing(norm_q10_all).T, header=header)
#
# Final adjustment and save
#
axes[1, 0].legend(loc='lower left', bbox_to_anchor=(1.05, 1.075))
grid.tight_layout(fig, pad=0.6, rect=[0.04, 0, 1, 1])
grid.update(wspace=0.12, hspace=0.0)
plt.savefig('%s/fitting-and-validation-eyring-q102-%s-v2.png' % (savedirlr,
prt), bbox_inch='tight', pad_inches=0, dpi=300)
plt.savefig('%s/fitting-and-validation-eyring-q102-%s-v2.png' % (savedir, prt),
bbox_inch='tight', pad_inches=0, dpi=500)
print('Done')