def plotMetric(t_val, t_offset, val, baseline, color, showYerr = True, showXerr = True):
if fun_lib.getObjectiveByName(objectives, val).costFunctionType == fun_lib.CostFunctionType.Ignore:
return
val_mean = getTrgtVal(val)*baseline
val_std = getTrgtVar(val)*baseline if showYerr else None
t_mean = getTrgtVal(t_val) + t_offset
t_std = getTrgtVar(t_val) if showXerr else None
costs_legend.append('%s %.4f\n%s %.4f' % (val, getCost(val), t_val, getCost(t_val)))
# zorder 3 is a workaround to show errorbars above the plots
ax.errorbar(t_mean, val_mean, yerr= val_std, xerr=t_std, fmt = color, barsabove = True, zorder=3, linewidth = 2)
def plotObjectives(vars_set, interval, objectives):
if '__plot_axes' in vars_set:
ax = vars_set['__plot_axes']
else:
fig = plt.figure()
ax = fig.subplots()
ax.plot(vars_set['time'],
vars_set['brachial_pressure'] / 133.32,
label='Brachial pressure mmHg')
ax.plot(vars_set['time'], vars_set['CO'] * 1000 * 60, label='CO l/min')
ax.plot(vars_set['time'],
vars_set['renal_capillary'] / 133.32,
label='Capillary pressure')
# ax.plot([vars_set['time'][interval[0]], vars_set['time'][interval[-1]]], [fun_lib.getObjectiveByName(objectives, 'EF').value*100]*2, label='EF')
ax.plot([vars_set['time'][interval[0]], vars_set['time'][interval[-1]]],
[fun_lib.getObjectiveByName(objectives, 'PWV').value * 1] * 2,
label='PWV')
# bounds
pack = (objectives, vars_set['time'], ax, interval)
fun_lib.plotObjectiveTarget(pack, 'BPs', 1 / 133.32)
fun_lib.plotObjectiveTarget(pack, 'BPd', 1 / 133.32)
# fun_lib.plotObjectiveTarget(pack,'CO', 1000*60)
fun_lib.plotObjectiveTarget(pack, 'BPk', 1 / 133.32)
# fun_lib.plotObjectiveTarget(pack,'EF', 100)
fun_lib.plotObjectiveTarget(pack, 'HR', 60, verticalalignment='top')
# fun_lib.plotObjectiveTarget(pack,'Ppa', 1/133.32)
fun_lib.plotObjectiveTarget(pack, 'Ppas', 1 / 133.32)
fun_lib.plotObjectiveTarget(pack, 'Ppad', 1 / 133.32)
fun_lib.plotObjectiveTarget(pack, 'Ppv', 1 / 133.32)
fun_lib.plotObjectiveLimit(pack,
'PWV',
1,
'lower',
verticalalignment='top')
total_costs = fun_lib.countTotalWeightedCost(objectives)
ax.set_title('Baseline costs %.6f' % total_costs)
ax.set_ylim([0, 140])
def getCost(name):
return fun_lib.getObjectiveByName(objectives, name).cost()
def getObj(name):
if fun_lib.getObjectiveByName(objectives, name).costFunctionType == fun_lib.CostFunctionType.Ignore:
return -100
return fun_lib.getObjectiveByName(objectives, name).value
def getTrgtVar(name):
return fun_lib.getObjectiveByName(objectives, name).std
def getTrgtVal(name):
return fun_lib.getObjectiveByName(objectives, name).targetValue
def buildPPObjective(po):
(name, timeObjName, phase, tolerance, include_in_cost) = po
o = fun_lib.getObjectiveByName(objectives, timeObjName).value + phase[0]
i = fun_lib.findLowestIndex(o, time)
value = ppulse[i]/baseline_pp
return fun_lib.ObjectiveVar(name, value=value, costFunctionType=include_in_cost, base=baseline_pp, tolerance=1/baseline_pp)
def plotTargetValues(ax, objectives, valsalva_start, valsalva_end, time):
# plot merged timecourse
# valsalva_start = 20
# valsalva_end = 35
# signal_end = 55
signal_end = time[-1]
# All targets are relative to baseline, so we have to have that set first
baseline_bp = fun_lib.getObjectiveByName(objectives, 'baseline_bp').value
baseline_hr = fun_lib.getObjectiveByName(objectives, 'baseline_hr').value
baseline_pp = fun_lib.getObjectiveByName(objectives, 'baseline_pp').value
# just a shortcuts
def getTrgtVal(name):
return fun_lib.getObjectiveByName(objectives, name).targetValue
def getTrgtVar(name):
return fun_lib.getObjectiveByName(objectives, name).std
def getCost(name):
return fun_lib.getObjectiveByName(objectives, name).cost()
# color for BP and HR
c_BP = 'g'
c_HR = 'c'
c_PP = 'm'
# baselines
ax.errorbar((valsalva_start/2), getTrgtVal('baseline_bp'), yerr=getTrgtVar('baseline_bp'), fmt = c_BP, barsabove = True, zorder=3)
ax.errorbar((valsalva_start/2), getTrgtVal('baseline_hr'), yerr=getTrgtVar('baseline_hr'), fmt = c_HR, barsabove = True, zorder=3)
ax.errorbar((valsalva_start/2), getTrgtVal('baseline_pp'), yerr=getTrgtVar('baseline_pp'), fmt = c_PP, barsabove = True, zorder=3)
# recoveries
ax.errorbar((signal_end - 2.5), getTrgtVal('ph5_recovery')*baseline_bp, yerr=getTrgtVar('ph5_recovery')*baseline_bp, fmt = c_BP, barsabove = True, zorder=3)
ax.errorbar((signal_end - 2.5), getTrgtVal('ph5_hr_recovery')*baseline_hr, yerr=getTrgtVar('ph5_hr_recovery')*baseline_hr, fmt = c_HR, barsabove = True, zorder=3)
# sv lower limit
if len([o for o in objectives if o.name == 'SV_min_valsalva']) > 0:
pack = (objectives, time, ax, fun_lib.findInterval(valsalva_start, valsalva_end, time))
fun_lib.plotObjectiveLimit(pack, 'SV_min_valsalva', 1, 'lower')
fun_lib.plotObjectiveLimit(pack, 'SV_min_recovery', 1, 'lower')
costs_legend = []
def plotMetric(t_val, t_offset, val, baseline, color, showYerr = True, showXerr = True):
if fun_lib.getObjectiveByName(objectives, val).costFunctionType == fun_lib.CostFunctionType.Ignore:
return
val_mean = getTrgtVal(val)*baseline
val_std = getTrgtVar(val)*baseline if showYerr else None
t_mean = getTrgtVal(t_val) + t_offset
t_std = getTrgtVar(t_val) if showXerr else None
costs_legend.append('%s %.4f\n%s %.4f' % (val, getCost(val), t_val, getCost(t_val)))
# zorder 3 is a workaround to show errorbars above the plots
ax.errorbar(t_mean, val_mean, yerr= val_std, xerr=t_std, fmt = color, barsabove = True, zorder=3, linewidth = 2)
def plotBPMetric(t_val, t_offset, val, color = c_BP):
plotMetric(t_val, t_offset, val, baseline_bp, color)
def plotHRMetric(t_val, t_offset, val, color = c_HR):
plotMetric(t_val, t_offset, val, baseline_hr, color)
def plotPPMetric(t_val, t_offset, val, color = c_PP):
plotMetric(t_val, t_offset, val, baseline_pp, color, showXerr=False)
plotBPMetric('t_ph1_peak', valsalva_start, 'ph1_peak')
plotBPMetric('t_ph2_mean_min', valsalva_start, 'ph2_mean_min')
plotBPMetric('t_ph2_max', valsalva_end, 'ph2_max')
plotBPMetric('t_ph4_drop', valsalva_end, 'ph4_drop')
plotBPMetric('t_ph4_ovrshoot', valsalva_end, 'ph4_ovrshoot')
plotHRMetric('t_ph1_hr_min', valsalva_start, 'ph1_hr_min')
plotHRMetric('t_ph4_hr_max', valsalva_end, 'ph4_hr_max')
plotHRMetric('t_ph4_hr_drop', valsalva_end, 'ph4_hr_drop')
plotPPMetric('t_ph2_mean_min', valsalva_start, 'pp_ph2_mean_min')
plotPPMetric('t_ph2_max', valsalva_end, 'pp_ph2_max')
plotPPMetric('t_ph4_drop', valsalva_end, 'pp_ph4_drop')
ax.text(0, 60, '\n'.join(costs_legend),
horizontalalignment='left',
verticalalignment='bottom', fontsize = 8)
pass