def plot_tissue_radius_difference_vs_hb_mean(self, **kwargs):
self.plot(self.results.hb_mean_on_edge,
self.results.functional_minus_topological_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['difference'], **kwargs))
setXLabel(r'\mathrm{average\;saturation}\;S', '')
setYLabel('r_{t,\mathrm{func}} - r_{t,\mathrm{topol}}', 'um')
def plotPO2ProfileInterstitialSpace(simParams, radius=17.6e-6, **kwargs):
kroghSol = KroghSolution2DCone(simParams)
kroghSol.intravascularResistanceLDHalf = kwargs.get(
'K0', kroghSol.intravascularResistanceLDHalf)
if 'K0' in kwargs:
print "plotPO2Profile: Using K0 = %g" % kwargs['K0']
style = kwargs.get('style', {'color': 'k'})
L = simParams['domainLength']
nx = 100
xValues = np.linspace(0, L, nx)
PO2 = [kroghSol.PO2Tissue(x, radius) for x in xValues]
plt.plot(1e6 * xValues, PO2, linewidth=1, **style)
plt.ylim(rounded_bounds(PO2, 10))
interstitialLayerWidth = 0.35e-6
kroghSol.geometry['radiusWall'] += interstitialLayerWidth
kroghSol.intravascularResistanceLDHalf *= 1.1
PO2WithInterstitial = [kroghSol.PO2Tissue(x, radius) for x in xValues]
plt.plot(1e6 * xValues, PO2WithInterstitial, '--')
labels.setXLabel('x', 'um')
labels.setYLabel('PO2', 'mmHg')
print 'Difference: ', np.array(PO2) - np.array(PO2WithInterstitial)
def plotPO2YProfile(simParams, x=150e-6, **kwargs):
kroghSol = KroghSolution2DCone(simParams)
kroghSol.intravascularResistanceLDHalf = kwargs.get(
'K0', kroghSol.intravascularResistanceLDHalf)
kroghSol.convO2Transport = kwargs.get('convO2Transport', True)
if 'K0' in kwargs:
print "plotPO2Profile: Using K0 = %g" % kwargs['K0']
style = kwargs.get('style', {'color': 'k'})
PO2AnalyticalStyle = {
'color': 'k',
'linestyle': '-',
'linewidth': 1,
'dashes': (1.2, 1.5)
}
# 'dashes': (5,2.5,1,2.5)}
nr = 300
rMax = kroghSol.geometry.tissueRadius(x)
rAnalytical = np.linspace(0, rMax, nr)
rTissue = np.linspace(kroghSol.geometry['radiusWall'], rMax, nr)
PO2Analytical = [kroghSol.PO2Analytical(x, r) for r in rAnalytical]
PO2Tissue = [kroghSol.PO2Tissue(x, r) for r in rTissue]
PO2RBCMean = kroghSol.PO2RBCMeanAtX(x)
plt.plot(1e6 * rAnalytical, PO2Analytical, **PO2AnalyticalStyle)
plt.plot(1e6 * rTissue, PO2Tissue, linewidth=1, **style)
plt.plot(1e6 * kroghSol.Rrbc / 2, PO2RBCMean, '.', color='k', markersize=4)
plt.xlim(0, 1e6 * rMax)
plt.ylim(rounded_bounds(PO2Analytical, 10))
labels.setXLabel('r', 'um')
labels.setYLabel('PO2', 'mmHg')
def plot_decay_time(diff_interaction_coeffs, manual_labels=False):
"""
Plot the decay time for diffusive interaction as a function of linear density
and hemoglobin saturation
Args:
diff_interaction_coeffs (DiffusiveInteractionResistanceAnalytical)
"""
ld_vals = np.linspace(0.1, 0.9, 81)
hb_vals = np.linspace(0.1, 0.9, 81)
xx, yy = np.meshgrid(ld_vals, hb_vals)
decay_times = diff_interaction_coeffs.decay_time(xx, yy)
t_levels = contourLevels(decay_times, 0.03, 0.03)
manual_locations = [(0.221, 0.813), (0.370, 0.700), (0.506, 0.601),
(0.648, 0.508), (0.796, 0.397)]
c_plot = plt.contour(xx,
yy,
decay_times,
t_levels,
colors=None,
cmap=plt.cm.jet)
if manual_labels:
plt.clabel(c_plot,
inline=True,
inline_spacing=40,
fontsize=8,
fmt='%g',
manual=manual_locations)
else:
plt.clabel(c_plot, inline=1, fontsize=7, fmt='%g')
# print [(text._x, text._y) for text in c_plot.cl] # display label positions
setXLabel('LD', '')
setYLabel('S', '')
def plot_histogram_tissue_radii(self, **kwargs):
style_scheme = styles.StyleSchemeCOSH()
x_range = (0, 40)
bin_width = 2
bar_fraction = 0.7
bar_width = 0.5 * bin_width * bar_fraction
bins = np.array(
np.linspace(x_range[0], x_range[1],
(x_range[1] - x_range[0]) / bin_width + 1))
ax = plt.gca()
func_radii = 1e6 * np.array(self.results.functional_tissue_radii())
topol_radii = 1e6 * np.array(self.results.topological_tissue_radii())
functional_count, _ = np.histogram(func_radii, bins=bins)
topological_count, _ = np.histogram(topol_radii, bins=bins)
x1 = 0.5 * (bins[:-1] + bins[1:]) - 0.5 * bar_width
x2 = 0.5 * (bins[:-1] + bins[1:]) + 0.5 * bar_width
plt.bar(x1,
functional_count,
bar_width,
edgecolor=style_scheme['int_func_radii_edge_color'],
facecolor=style_scheme['int_func_radii_face_color'],
linewidth=0.4,
label='functional')
plt.bar(x2,
topological_count,
bar_width,
edgecolor=style_scheme['int_topol_radii_edge_color'],
facecolor=style_scheme['int_topol_radii_face_color'],
linewidth=0.4,
label='geometric')
# plot mean and standard deviation
y_max = np.max(np.hstack((functional_count, topological_count)))
ax.errorbar(np.mean(func_radii),
1.1 * y_max,
xerr=np.std(func_radii),
fmt='o',
markersize=2,
linewidth=1,
elinewidth=0.5,
capsize=2,
capthick=0.5,
color=style_scheme['int_func_radii_face_color'])
ax.errorbar(np.mean(topol_radii),
1.05 * y_max,
xerr=np.std(topol_radii),
fmt='o',
markersize=2,
linewidth=1,
elinewidth=0.5,
capsize=2,
capthick=0.5,
color=style_scheme['int_topol_radii_edge_color'])
ax.set_xlim(x_range)
majorFormatter = FormatStrFormatter('%d')
ax.yaxis.set_major_formatter(majorFormatter)
styles.create_COSH_legend(ax, bbox_to_anchor=(1, 0.90))
setXLabel('\mathrm{tissue\;radius}\;r_t', 'um')
setYLabel('\mathrm{frequency}', '')
def plotSimHbProfile(self, nProfiles, **kwargs):
"""Plot individual simulated hemoblogin saturation profiles."""
style = kwargs.get('style', style_scheme['individualRBC'])
S = self.postprocessor.rbcDataPostProcessor.fieldOnLastPaths(
'Hb_mean', self.postprocessor.sValues(), nProfiles)
plt.plot(1e6 * self.xValues, S, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('S', '')
def plotNoModelStdHbProfile(self, **kwargs):
style = kwargs.get('style', style_scheme['stdNoModel'])
plt.plot(
1e6 * self.xValues,
self.postprocessor.initialStdSaturation() *
np.ones(self.xValues.shape), **style)
labels.setXLabel('x', 'um')
labels.setYLabel('\sigma_S', '')
def plot_functional_vs_topological_tissue_radii(self, **kwargs):
self.plot(self.results.topological_tissue_radii,
self.results.functional_tissue_radii,
x_factor=1e6,
y_factor=1e6,
**dict(self.volume_type_symbols['difference'], **kwargs))
setXLabel('r_{t,\mathrm{geom}}', 'um')
setYLabel('r_{t,\mathrm{func}}', 'um')
def plotODEMeanHbProfile(self, **kwargs):
"""Plot the mean hemoglobin saturation profiles from the ODE model.
"""
lineStyle = kwargs.get('style', style_scheme['meanRBCInter'])
SMean = self.postprocessor.meanSaturationODE()
plt.plot(1e6 * self.xValues, SMean, **lineStyle)
labels.setXLabel('x', 'um')
labels.setYLabel('S', '')
def plotSimStdHbProfile(self, **kwargs):
"""Plot the standard deviation of the simulated hemoblogin saturation"""
style = kwargs.get('style', style_scheme['stdSim'])
SStd = self.postprocessor.rbcDataPostProcessor.fieldStd(
'Hb_mean', self.postprocessor.sValues(),
self.postprocessor.nAverage)
plt.plot(1e6 * self.xValues, SStd, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('\sigma_S', '')
def plotExponentialFitToHbDifference(self, **kwargs):
"""
Plot the exponential fit to the simulated hemoglobin saturation difference.
"""
style = kwargs.get('style', {'dashes': (1,2), 'color': 'r'})
fittedHbDiff = self.postprocessor.exponentialFitToHbDifference()
plt.plot(1e6*self.postprocessor.xValues, fittedHbDiff, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('\Delta S', '')
def contour_time_scale(self):
levels = np.arange(0, 2, 0.1)
vrbc, ld = self.post_processor.param_study.meshGrid()
time_scale = self._reshape_to_grid(self.post_processor.call_post_processor_method(
'exponential_fit_decay_time'))
c1 = plt.contour(ld, 1e3*vrbc, time_scale, levels, cmap=plt.cm.jet)
labels.setXLabel('LD', '-')
labels.setYLabel('vrbc', 'mm/s')
plt.clabel(c1, inline=1, fontsize=12, fmt='%g')
def plotODEStdHbProfile(self, **kwargs):
"""Plots the standard deviation of the hemoglobin saturation from the
ODE model.
"""
style = kwargs.get('style', style_scheme['stdRBCInter'])
SStd = self.postprocessor.stdSaturationODE()
plt.plot(1e6 * self.xValues, SStd, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('\sigma_S', '')
def plotODELinearizedStdHbProfile(self, **kwargs):
"""Plots the standard deviation of the hemoglobin saturation from the
linearized ODE.
"""
style = kwargs.get('style', style_scheme['stdLinear'])
SLinStd = self.postprocessor.linearizedStdSaturationODE()
plt.plot(1e6 * self.xValues, SLinStd, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('\sigma_S', '')
def contour_delta_po2(self):
levels = np.arange(2, 27, 2)
vrbc, ld = self.post_processor.param_study.meshGrid()
delta_p_simul = self._reshape_to_grid(self.post_processor.delta_p_tissue_simul())
delta_p_fitted = self._reshape_to_grid(self.post_processor.delta_p_tissue_fitted())
c1 = plt.contour(ld, 1e3*vrbc, delta_p_simul, levels, cmap=plt.cm.jet)
c2 = plt.contour(ld, 1e3*vrbc, delta_p_fitted, levels, linestyles='dashed', cmap=plt.cm.jet)
labels.setXLabel('LD', '-')
labels.setYLabel('vrbc', 'mm/s')
plt.clabel(c1, inline=1, fontsize=12, fmt='%g')
def plotNoModelMeanPlusMinusStdHbProfile(self, **kwargs):
"""Plot the mean +/- std of the hemoglobin saturation with no RBC interaction modeling.
"""
style = kwargs.get('style', style_scheme['meanPmStdNoModel'])
SMean = self.postprocessor.meanSaturationNoModel()
SStd = self.postprocessor.stdSaturationNoModel()
plt.plot(1e6 * self.xValues, SMean + SStd, **style)
plt.plot(1e6 * self.xValues, SMean - SStd, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('S', '')
def plotODEMeanPlusMinusStdHbProfile(self, **kwargs):
"""Plot the mean +/- std of the hemoglobin saturation from the ODE model.
"""
lineStyle = kwargs.get('style', style_scheme['meanPmStdRBCInter'])
SMean = self.postprocessor.meanSaturationODE()
SStd = self.postprocessor.stdSaturationODE()
plt.plot(1e6 * self.xValues, SMean + SStd, **lineStyle)
plt.plot(1e6 * self.xValues, SMean - SStd, **lineStyle)
labels.setXLabel('x', 'um')
labels.setYLabel('S', '')
def plotExponentialFitStdHb(self, **kwargs):
"""
Plot the exponential fit to the standard deviation of the simulated hemoglobin
saturation.
"""
style = kwargs.get('style', style_scheme['stdExpFit'])
fittedStd = self.postprocessor.exponentialFitToStdHb()
plt.plot(1e6 * self.xValues, fittedStd, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('\sigma_S', '')
def plotSimMeanHbProfile(self, **kwargs):
"""Plot the mean simulated hemoblogin saturation"""
style = kwargs.get('style', style_scheme['meanSim'])
# S = self.rbcDataPostProcessor.fieldAverageAlternating('Hb_mean',
# self.sValues(), 2, self.nAverage)
SMean = self.postprocessor.rbcDataPostProcessor.fieldAverage(
'Hb_mean', self.postprocessor.sValues(),
self.postprocessor.nAverage)
plt.plot(1e6 * self.xValues, SMean, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('S', '')
def plot_tissue_radii_vs_hb_drop(self, **kwargs):
self.plot(self.results.hb_mean_drop,
self.results.topological_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['topological'], **kwargs))
self.plot(self.results.hb_mean_drop,
self.results.functional_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['functional'], **kwargs))
setXLabel(r'S_a - S_v', '')
setYLabel('\mathrm{tissue\;radius}\;r_t', 'um')
def plot_tissue_radii_vs_edge_index(self, **kwargs):
self.plot(self.results.edge_ids,
self.results.topological_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['topological'], **kwargs))
self.plot(self.results.edge_ids,
self.results.functional_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['functional'], **kwargs))
setXLabel('e_i', '')
setYLabel('\mathrm{tissue\;radius}\;r_t', 'um')
def plot_tissue_radii_vs_hematocrit(self, **kwargs):
self.plot(self.results.hematocrit,
self.results.topological_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['topological'], **kwargs))
self.plot(self.results.hematocrit,
self.results.functional_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['functional'], **kwargs))
setXLabel('\mathrm{tube\;hematocrit}\;H_T', '')
setYLabel('\mathrm{tissue\;radius}\;r_t', 'um')
def plotAveragedProbes(EAT_dicts, probes, from_time, **kwargs):
nProbes = len(probes)
# extract probe positions (scaled to mum)
xProbes = [1e6 * probe['positions'][0][0] for probe in probes]
# extract PO2 max, min and mean
from_idx = np.searchsorted(EAT_dicts[0]['t_front'], from_time)
PO2_max = [EAT_dict['PO2_max'][from_idx:] for EAT_dict in EAT_dicts]
PO2_min = [EAT_dict['PO2_min'][from_idx:] for EAT_dict in EAT_dicts]
PO2_mean = [EAT_dict['PO2_mean'][from_idx:] for EAT_dict in EAT_dicts]
mean_PO2_max = np.asarray([np.mean(P) for P in PO2_max])
mean_PO2_min = np.asarray([np.mean(P) for P in PO2_min])
mean_PO2_mean = np.asarray([np.mean(P) for P in PO2_mean])
mean_EAT = mean_PO2_max - mean_PO2_min
style = kwargs.get('linestyle', {
'markerfacecolor': 'None',
'markersize': 5,
'linewidth': 0.4
})
plt.plot(xProbes, mean_PO2_max, 'ro-', label=r'RBC', **style)
# plt.plot(xProbes, mean_PO2_max, 'r-', label=r'RBC', **style)
plt.plot(xProbes, mean_PO2_mean, 'ks-', label=r'mean', **style)
plt.plot(xProbes, mean_PO2_min, 'b^-', label=r'inter-RBC', **style)
# plt.plot(xProbes, mean_PO2_min, 'b-', label=r'inter-RBC', **style)
plt.plot(xProbes, mean_EAT, 'k--', dashes=(5, 3), label='EAT', **style)
labels.setXLabel('x', 'um')
labels.setYLabel('PO2', 'mmHg')
# plt.legend()
plt.ylim([0, 80])
# plt.xlim([8.6, 91.4])
# plt.xlim([10, 90])
print 'Mean PO2 max = ', mean_PO2_max
print 'Mean PO2 min = ', mean_PO2_min
print 'Mean PO2 mean = ', mean_PO2_mean
print 'Mean EAT = ', mean_EAT
print 'Average mean PO2 max = ', np.mean(mean_PO2_max)
print 'Average mean PO2 min = ', np.mean(mean_PO2_min)
print 'Average mean PO2 mean = ', np.mean(mean_PO2_mean)
print 'Average mean EAT = ', np.mean(mean_EAT)
print 'Differences over 50 mum:'
print 'Delta PO2 max = ', mean_PO2_max[:-5] - mean_PO2_max[-4:]
print 'Delta PO2 min = ', mean_PO2_min[:-5] - mean_PO2_min[-4:]
print 'Delta PO2 mean = ', mean_PO2_mean[:-5] - mean_PO2_mean[-4:]
def plotHillEquation(bloodChem, P, reversed=False):
S = bloodChem.hillS(P)
if reversed:
plt.plot(S, P)
labels.setXLabel('S', '-')
labels.setYLabel('P', 'mmHg')
plt.xlim(xmax=1.0)
else:
plt.plot(P, S)
labels.setXLabel('P', 'mmHg')
labels.setYLabel('S', '-')
plt.ylim(ymax=1.0)
def plot_tissue_radii_vs_arrival_path_length(self, **kwargs):
self.plot(self.results.mean_arrival_path_length,
self.results.topological_tissue_radii,
x_factor=1e6,
y_factor=1e6,
**dict(self.volume_type_symbols['topological'], **kwargs))
self.plot(self.results.mean_arrival_path_length,
self.results.functional_tissue_radii,
x_factor=1e6,
y_factor=1e6,
**dict(self.volume_type_symbols['functional'], **kwargs))
setXLabel('\mathrm{arrival\;path\;length}\;ts', 'um')
setYLabel('\mathrm{tissue\;radius}\;r_t', 'um')
def plot_tissue_radii_vs_arrival_transit_time(self, **kwargs):
self.plot(self.results.mean_arrival_transit_time,
self.results.topological_tissue_radii,
x_factor=1e3,
y_factor=1e6,
**dict(self.volume_type_symbols['topological'], **kwargs))
self.plot(self.results.mean_arrival_transit_time,
self.results.functional_tissue_radii,
x_factor=1e3,
y_factor=1e6,
**dict(self.volume_type_symbols['functional'], **kwargs))
setXLabel('\mathrm{arrival\;transit\;time}\;tt', 'ms')
setYLabel('\mathrm{tissue\;radius}\;r_t', 'um')
def plot_tissue_radii_vs_rbc_velocity(self, **kwargs):
self.plot(self.results.rbc_velocity,
self.results.topological_tissue_radii,
x_factor=1e3,
y_factor=1e6,
**dict(self.volume_type_symbols['topological'], **kwargs))
self.plot(self.results.rbc_velocity,
self.results.functional_tissue_radii,
x_factor=1e3,
y_factor=1e6,
**dict(self.volume_type_symbols['functional'], **kwargs))
setXLabel('\mathrm{RBC\;velocity}\;v_{\mathrm{rbc}}', '\mathrm{mm/s}')
setYLabel('\mathrm{tissue\;radius}\;r_t', 'um')
def plot_probe(probe, probe_idx, min_time, max_time, **kwargs):
linestyle = kwargs.get('linestyle', {'linestyle': '-'})
times = probe.times(min_time=min_time, max_time=max_time)
ymax = 0
if probe_idx is None:
probe_idx = probe.position_indices()
for i in probe_idx:
values = probe.values(position=i, min_time=min_time, max_time=max_time)
plt.plot(times, values, **linestyle)
setXLabel('t', 's')
setYLabel('PO2', 'mmHg')
ymax = np.max([ymax, np.max(values)])
plt.xlim([min(times) - 1e-6, max(times) + 1e-6])
def plotSimMeanPlusMinusHbProfile(self, **kwargs):
"""Plot the mean simulated hemoblogin saturation"""
style = kwargs.get('style', style_scheme['meanPmStdSim'])
SMean = self.postprocessor.rbcDataPostProcessor.fieldAverage(
'Hb_mean', self.postprocessor.sValues(),
self.postprocessor.nAverage)
SStd = self.postprocessor.rbcDataPostProcessor.fieldStd(
'Hb_mean', self.postprocessor.sValues(),
self.postprocessor.nAverage)
plt.plot(1e6 * self.xValues, SMean + SStd, **style)
plt.plot(1e6 * self.xValues, SMean - SStd, **style)
labels.setXLabel('x', 'um')
labels.setYLabel('S', '')
def plot_tissue_radii_vs_rbc_flow(self, **kwargs):
kwargs_topol, kwargs_func = self.append_label_with_geom_and_func(
**kwargs)
self.plot(self.results.rbc_flow,
self.results.topological_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['topological'],
**kwargs_topol))
self.plot(self.results.rbc_flow,
self.results.functional_tissue_radii,
y_factor=1e6,
**dict(self.volume_type_symbols['functional'],
**kwargs_func))
setXLabel('\mathrm{RBC\;flow}', '\mathrm{s}^{-1}')
setYLabel('\mathrm{tissue\;radius}\;r_t', 'um')
