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 plot_histogram_tissue_radii_multipanel(self, functional_colors,
functional_labels, **kwargs):
f, axarr = plt.subplots(len(self.results_list) + 2, sharex=True)
panel_annotation = 'ABCD'
style_scheme = styles.StyleSchemeCOSH()
x_range = (0, 40)
bin_width = 2
bar_fraction = 0.7
bar_width = bin_width * bar_fraction
bins = np.array(
np.linspace(x_range[0], x_range[1],
(x_range[1] - x_range[0]) / bin_width + 1))
bin_center = 0.5 * (bins[:-1] + bins[1:])
y_max = 0.0
hist_axes = axarr[0:-1]
# boxplot
data = [
1e6 * np.array(self.results_list[0].topological_tissue_radii()),
1e6 * np.array(self.results_list[1].functional_tissue_radii()),
1e6 * np.array(self.results_list[0].functional_tissue_radii())
]
labels = 'CBA'
medianprops = {'color': 'w'}
flierprops = {'marker': '.', 'markersize': 2}
bp = axarr[-1].boxplot(data,
whis=[5, 95],
vert=False,
labels=labels,
patch_artist=True,
medianprops=medianprops,
showfliers=False,
flierprops=flierprops)
fill_colors = [
style_scheme['int_topol_radii_face_color'], functional_colors[1],
functional_colors[0]
]
for patch, color in zip(bp['boxes'], fill_colors):
patch.set_facecolor(color)
# histograms
for i, (results, ax) in enumerate(zip(self.results_list, hist_axes)):
func_radii = 1e6 * np.array(results.functional_tissue_radii())
functional_count, _ = np.histogram(func_radii, bins=bins)
y_max = max(y_max, np.max(functional_count))
x_func = bin_center
ax.bar(x_func,
functional_count,
bar_width,
edgecolor='k',
facecolor=functional_colors[i],
linewidth=0.4,
label=functional_labels[i])
x_topol = bin_center
topol_radii = 1e6 * np.array(
self.results_list[0].topological_tissue_radii())
topological_count, _ = np.histogram(topol_radii, bins=bins)
axarr[-2].bar(x_topol,
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')
y_max = max(y_max, np.max(topological_count))
# plot mean and standard deviation
y_error_bar = 1.04 * y_max
for i, (results, ax) in enumerate(zip(self.results_list, hist_axes)):
func_radii = 1e6 * np.array(results.functional_tissue_radii())
ax.errorbar(np.mean(func_radii),
y_error_bar,
xerr=np.std(func_radii),
fmt='o',
markersize=2,
linewidth=1,
elinewidth=0.5,
capsize=2,
capthick=0.5,
color=functional_colors[i])
axarr[-2].errorbar(np.mean(topol_radii),
y_error_bar,
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'])
axarr[0].set_xlim(x_range[0] - 1, x_range[1] + 1)
y_lim_max = 1.1 * y_max
setXLabel('\mathrm{tissue\;radius}\;r_t', 'um')
for ax in hist_axes:
majorLocator = MultipleLocator(10)
ax.yaxis.set_major_locator(majorLocator)
ax.set_ylim((0, y_lim_max))
styles.create_COSH_legend(ax,
loc='upper left',
bbox_to_anchor=(0.01, 0.92))
setYLabel('\mathrm{frequency}', '', ax=ax)
for ax, annotation in zip(axarr, panel_annotation):
annotate_axis_corner(ax, annotation, xy=(0.94, 0.82))
"""
Plotter objects for hemoglobin saturation in a capillary network.
"""
from __future__ import division
import copy
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import numpy as np
from HbO2.plot import styles
from HbO2.plot.labels import setXLabel, setYLabel
from plot.utils import annotate_axis_corner
style_scheme = styles.StyleSchemeCOSH()
class TissueVolumePlotter(object):
"""
Plotter object for tissue volumes in a capillary network.
"""
volume_type_symbols = {
'functional': {
'marker': '^',
'markeredgecolor': style_scheme['int_func_radii_face_color'],
'markerfacecolor': (1, 1, 1, 0)
},
'topological': {
'marker': 'o',
'markersize': 2.5,
def plot_histogram_tissue_radii(self, functional_colors, functional_labels,
**kwargs):
ax = plt.gca()
style_scheme = styles.StyleSchemeCOSH()
n_bar_types = 1 + len(self.results_list)
x_range = (0, 40)
bin_width = 2
bar_fraction = 0.7
bar_width = bin_width * bar_fraction / n_bar_types
bins = np.array(
np.linspace(x_range[0], x_range[1],
(x_range[1] - x_range[0]) / bin_width + 1))
bin_center = 0.5 * (bins[:-1] + bins[1:])
y_max = 0.0
for i, results in enumerate(self.results_list):
func_radii = 1e6 * np.array(results.functional_tissue_radii())
functional_count, _ = np.histogram(func_radii, bins=bins)
y_max = max(y_max, np.max(functional_count))
offset = (-0.5 +
(i + 0.5) / n_bar_types) * bin_width * bar_fraction
x_func = bin_center + offset
plt.bar(x_func,
functional_count,
bar_width,
edgecolor='k',
facecolor=functional_colors[i],
linewidth=0.4,
label=functional_labels[i])
offset = (0.5 - 0.5 / n_bar_types) * bin_width * bar_fraction
x_topol = bin_center + offset
topol_radii = 1e6 * np.array(
self.results_list[0].topological_tissue_radii())
topological_count, _ = np.histogram(topol_radii, bins=bins)
plt.bar(x_topol,
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 = max(y_max, np.max(topological_count))
for i, results in enumerate(self.results_list):
func_radii = 1e6 * np.array(results.functional_tissue_radii())
y_error_bar = (1.1 - i / (len(self.results_list)) * 0.07) * y_max
ax.errorbar(np.mean(func_radii),
y_error_bar,
xerr=np.std(func_radii),
fmt='o',
markersize=2,
linewidth=1,
elinewidth=0.5,
capsize=2,
capthick=0.5,
color=functional_colors[i])
y_error_bar = 1.03 * y_max
ax.errorbar(np.mean(topol_radii),
y_error_bar,
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)
styles.create_COSH_legend(ax,
bbox_to_anchor=(0.48, 0.85),
handlelength=2.3,
handleheight=0.4)
setXLabel('\mathrm{tissue\;radius}\;r_t', 'um')
setYLabel('\mathrm{frequency}', '')
Plot statistics of functional and topological volumes in a capillary network.
"""
import argparse
import matplotlib.pyplot as plt
import numpy as np
from HbO2.plot import styles as cosh_styles
from HbO2.plot.tissuevolumes import TissueVolumePlotter
from HbO2.postprocess.results import MultiCaseResults
from HbO2.postprocess.tissuevolumes import TissueVolumeResults
from plot.figureoptions import FigureOptions
from utilities.arguments import add_single_or_multi_case_group
style_scheme = cosh_styles.StyleSchemeCOSH()
plot_styles = [style_scheme['MVN1'], style_scheme['MVN2']]
parser = argparse.ArgumentParser()
add_single_or_multi_case_group(parser)
fig_options = FigureOptions(parser)
args = parser.parse_args()
multi_case = True if args.caseNames else False
if not multi_case:
case_paths = [args.caseName]
else:
case_paths = args.caseNames
fig_options.parseOptions(args)
results = [TissueVolumeResults(case_path) for case_path in case_paths]
plotters = [TissueVolumePlotter(res) for res in results]
