def plotShapeMetrics(resultfile, pdfpages):
ld = resultfile.obtain_data('ld')
# get data as time series
data = collections.defaultdict(list)
for groupname in resultfile.groupnames:
metrics = resultfile.obtain_data('shape_metrics', groupname)
for k, v in metrics.iteritems():
data[k].append(v)
data['time'].append(resultfile.obtain_data('time', groupname))
# plot it
for groupname in resultfile.groupnames[-1:]:
metrics = resultfile.obtain_data('shape_metrics', groupname)
curv, curv_weights = resultfile.obtain_data('curvature_samples',
groupname)
#pprint.pprint(metrics)
fig, axes = pyplot.subplots(2, 2, figsize=(8, 6))
text = []
text += [
'time : {}'.format(f2s(resultfile.obtain_data('time', groupname)))
]
text += [
'surface tension : {}'.format(
f2s(
float(resultfile.f['parameters/tumor'].
attrs['surface_tension'])))
]
text += ['sphericity : {}'.format(f2s(metrics['sphericity']))]
text += ['approx. radius : {}'.format(f2s(metrics['radius']))]
text = '\n'.join(text)
axes[0, 0].text(0.1, 0.1, text, fontsize=14)
mpl_utils.remove_frame(axes[0, 0])
h, bins = np.histogram(curv,
bins=10,
weights=curv_weights,
normed=True)
bins = 0.5 * (bins[1:] + bins[:-1])
axes[0, 1].bar(bins, h, width=bins[1] - bins[0])
axes[0, 1].set(title=ur'$\kappa$', xlabel=ur'[1/\u03BCm]', ylabel='N')
axes[1, 0].plot(data['time'], data['radius'], marker='x')
axes[1, 0].set(xlabel='$t$', ylabel=r'$r_{tum}$')
v, y0 = linearFit(data['time'], data['radius'])
x = np.asarray([0., data['time'][-1] * 1.1])
y = y0 + v * x
axes[1, 0].plot(x, y, label=ur'v = %s [\u03BCm/h]' % f2s(v))
axes[1, 0].legend()
axes[1, 1].set_visible(False)
fig.subplots_adjust(left=0.10,
right=0.95,
top=0.95,
bottom=0.10,
wspace=0.2,
hspace=0.2)
pdfpages.savefig(fig)
def PlotField(self,
ax,
sampleIndex=None,
xcoord=None,
color='k',
label='',
addXPositionLabel=False,
addXMark=False,
addYMark=False,
marker=None):
if sampleIndex is None:
sampleIndex = np.searchsorted(self.samples['x'], xcoord)
if marker is not None:
marker = niceMarker(marker, color)
else:
marker = dict(color=color)
p = self.samples['pos'][sampleIndex]
idx = self.po2fieldld.WorldToLattice(p)
idx -= self.po2fieldld.box.reshape(3, 2)[:, 0]
yv_field = np.asarray(self.po2field[idx[0], :, idx[2]])
if addXPositionLabel:
label += ' x = ' + myutils.f2s(
self.samples['x'][sampleIndex]) + ' $\mu m$'
if label:
marker['label'] = label
ax.plot(self.y_field, yv_field, **marker)
if addXMark:
ax.axvline(p[1], color='k', ls=':')
if addYMark:
po2 = self.samples['po2'][sampleIndex]
ax.axhline(po2, ls='-', color=color)
def plotForPresent(fd, writer, data):
dpi = 80
rc = matplotlib.rc
rc('figure', figsize=list(0.75 / dpi * np.asarray([1024, 768])), dpi=dpi)
rc('font', size=12.)
rc('axes') #, titlesize = 12., labelsize = 12.)
rc('pdf', compression=6, fonttype=42)
rc(
'figure', **{
'subplot.left': 0.04,
'subplot.right': 1. - 0.04,
'subplot.bottom': 0.06,
'subplot.top': 1. - 0.06,
'subplot.wspace': 0.1,
'subplot.hspace': 0.1
})
rc('savefig', facecolor='none', edgecolor='none', dpi=dpi)
rc('font', **{'family': 'sans-serif'}) #,'sans-serif':['Helvetica']})
rc('path', simplify_threshold=0.01)
#rc('lines', linewidth=2.)
for groupname in fd.groupnames:
print 'plt', groupname
group = fd.f[groupname]
fig = matplotlib.figure.Figure()
fig.suptitle('$t$=%s h' % (myutils.f2s(group.attrs['time'])))
#datanames = set(['conc', 'obstacle', 'vel_0', 'press', 'sources', 'ls'])
if 1:
ax1, ax2, (p1, p2), p3 = add_twinplot(fig, (2, 2, 1), fd,
groupname,
['conc', 'obstacle'])
ax1.legend()
ax1.xaxis.set_visible(False)
else:
ax1, ax2, (p1, ), p3 = add_twinplot(fig, (2, 2, 1), fd, groupname,
[
'conc',
])
ax1.legend()
ax1.xaxis.set_visible(False)
ax1, ax2, (p1, ), _ = add_twinplot(fig, (2, 2, 2), fd, groupname,
['press'])
ax1.legend([p1], [dataconfigs['press'].title])
ax1.xaxis.set_visible(False)
ax1, ax2, (p1, ), _ = add_twinplot(fig, (2, 2, 3), fd, groupname,
['vel_0'])
ax1.legend([p1], [dataconfigs['vel_0'].title])
ax1, ax2, (p1, ), _ = add_twinplot(fig, (2, 2, 4), fd, groupname,
['sources', 'oxy_sources'])
ax1.legend(
[p1],
[dataconfigs['sources'].title, dataconfigs['oxy_sources'].title])
pdf.savefig(fig)
def plot_data(ax, bins, data, std, color, label):
data = np.ma.filled(data, 0.)
std = np.ma.filled(std, 0.)
xy, clist = [], []
for i in xrange(len(data)):
clist.append(0.5 * (bins[i] + bins[i + 1]))
xy.append((bins[i], data[i]))
xy.append((bins[i + 1], data[i]))
xy = np.asarray(xy).transpose()
ax.plot(xy[0], xy[1], color=color, lw=1.)
ax.errorbar(clist,
data,
yerr=std,
lw=0,
color=color,
mew=2.,
elinewidth=1.,
capsize=10.,
label=label)
for c, d in zip(clist, data):
ax.text(c,
d,
'$' + myutils.f2s(d, prec=2, latex=True) + '$',
color=color)
def f2s(x):
return myutils.f2s(x, exponential=False, latex=False)
def printit(id, label, unit):
s = r'%s = %s +/- %s [%s]' % (label, myutils.f2s(
bv[id][0] * 60.), myutils.f2s(bv[id][1] * 60.), unit)
print s
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as pyplot
import mpl_utils
dpi = 80
rc = matplotlib.rc
rc('figure', figsize=list(np.asarray([1024, 768]) / dpi), dpi=dpi)
rc('font', size=11.)
rc('axes', titlesize=13., labelsize=13.)
rc('legend', fontsize=13.)
rc('pdf', compression=6, fonttype=42)
rc('savefig', facecolor='none', edgecolor='none', dpi=dpi)
rc('font', **{'family': 'sans-serif'}) #,'sans-serif':['Helvetica']})
rc('path', simplify_threshold=0.01)
df_formula = lambda x: (r'$\propto \epsilon ^ {- %s}$' % myutils.f2s(
x, latex=True))
fig, ax = pyplot.subplots(1, 1)
for df in dflist:
ax.plot(df.bs,
df.bc,
':kx',
label='measured' if df is dflist[0] else None)
avg_y0 = np.average([df.params[1] for df in dflist])
# ax.plot(dflist[0].bs, dflist[0].getY((-3., avg_y0)), 'r', label = df_formula(3.))
# ax.plot(dflist[0].bs, dflist[0].getY((-2., avg_y0)), 'g', label = df_formula(2.))
# ax.plot(dflist[0].bs, dflist[0].getY((-1., avg_y0)), 'b', label = df_formula(1.))
ax.plot(dflist[0].bs,
dflist[0].getY((-3., avg_y0)),
'-',
def printit(id, label, unit):
s = r'$%s$ = $%s \pm %s$ [$%s$]' % (
label, myutils.f2s(bv[id][0], latex=True),
myutils.f2s(bv[id][1], latex=True), unit)
return s
from plotIff import mk_LF_, mk_CM_
from matplotlib.ticker import MaxNLocator
import matplotlib
import matplotlib.pyplot as pyplot
import mpl_utils
LabelFactory = mk_LF_()
LF = LabelFactory
ColorMaps = mk_CM_()
CM = ColorMaps
mastersize = mpl_utils.a4size
a4size = mpl_utils.a4size
gridcolor = (0.7, 0.7, 0.7)
f2s = lambda x: myutils.f2s(x, latex=True)
import extractVtkFields, plotVessels
bins_rad = np.arange(0, 20000, 30.)
bins_dist = np.arange(-10000., 10000., 30.)
def get_tumld(tumorgroup):
p = tumorgroup['ptc'].attrs['LATTICE_PATH']
tum_ld = krebsutils.read_lattice_data_from_hdf(tumorgroup.file[p])
#field_ld is in root
#tum_ld = krebsutils.read_lattice_data_from_hdf(tumorgroup.parent.parent['field_ld'])
#tum_ld = krebsutils.read_lattice_data_from_hdf(tumorgroup.file[tumorgroup['conc'].attrs['LATTICE_PATH']])
return tum_ld
def printavg(name, data):
avg, std = np.average(data), np.std(data)
print '%s = %s +/- %s' % (name, f2s(avg), f2s(std))
def PlotRadial(pdfwriter, dataman, resultfiles, distance_distribution_name):
binspec = BinsSpecRange(-10000., 10000., 100.)
curves_by_path_and_name = myutils.MultiDict(list)
for f in files:
fm = myutils.MeasurementFile(f.f, h5files)
for outgroup_name in f.groupnames:
gvessels = f.f[outgroup_name]['vessels']
gtumor = f.f[outgroup_name]['tumor']
for name in [
'mvd', 'phi_vessels', 'shearforce', 'velocity', 'radius',
'maturation', 'flow'
]:
d = dataman.obtain_data('basic_vessel_radial', name, gvessels,
gtumor, 30., binspec,
distance_distribution_name, None,
(fm, outgroup_name))
curves_by_path_and_name[outgroup_name, name].append(d)
#tumor_radius = f.f[outgroup_name]['tumor'].attrs['TUMOR_RADIUS'] # this needs work ...
tumor_radius = dataman.obtain_data('approximate_tumor_radius',
gtumor)
curves_by_path_and_name[outgroup_name,
'approximate_tumor_radius'].append(
myutils.MeanValueArray(
1, tumor_radius, tumor_radius**2))
for k, v in curves_by_path_and_name.items():
curves_by_path_and_name[k] = myutils.MeanValueArray.fromSummation(
v_.avg for v_ in v)
# ---- here goes the plotting -----#
charsize = 12 / 90.
figscale = 1.8
fig, axes = pyplot.subplots(3,
2,
dpi=90,
figsize=(7. * figscale, 5. * figscale))
mpl_utils.subplots_adjust_abs(fig,
left=6 * charsize,
right=-2 * charsize,
top=-3. * charsize,
bottom=8. * charsize,
hspace=8. * charsize,
wspace=12 * charsize)
axes = axes.ravel()
default_colors = 'rgbcmykrgbcmykrgbcmyk'
pathorder = resultfiles[0].groupnames
labels_by_path = dict((g, 't=%s' % f2s(resultfiles[0].get_group_time(g)))
for g in resultfiles[0].groupnames)
bins = binspec.arange()
bins = 1.e-3 * np.average((bins[1:], bins[:-1]), axis=0)
ld = dataman.obtain_data('ld', resultfiles[0].f)
rmax = ld.GetWorldSize()[0] * 0.25 * 1.e-3
def plot(ax,
name,
scalefactor=1.,
label=None,
colors=default_colors,
errorbars=True,
zero_ylim=True):
for i, path in enumerate(pathorder):
curve = curves_by_path_and_name[path, name]
label = labels_by_path[path] if label else None
mask = ~curve.avg.mask
if errorbars:
mpl_utils.errorbar(ax,
bins[mask],
scalefactor * curve.avg[mask],
yerr=scalefactor * curve.std_mean[mask],
label=label,
marker=None,
color=colors[i],
every=2)
else:
ax.plot(bins[mask],
scalefactor * curve.avg[mask],
label=label,
color=colors[i])
if zero_ylim:
_, ymax = ax.get_ylim()
ax.set_ylim((0., ymax))
if distance_distribution_name == 'levelset':
ax.set(xlim=(-2., 2.))
mpl_utils.add_crosshair(ax, (0., None), ls=':')
else:
ax.set(xlim=(0., rmax))
for i, path in enumerate(pathorder):
mpl_utils.add_crosshair(
ax,
(curves_by_path_and_name[path,
'approximate_tumor_radius'].avg *
1.e-3, None),
ls=':')
def fmt_axis_labels(ax, name, scalefactor=None, hidex=True):
ax.set(
ylabel=(r'$%s$ %s' %
(Prettyfier.get_sym(name), Prettyfier.get_bunit(name))) +
((r' $\times\, %s$' %
f2l(scalefactor, exponential=True)) if scalefactor else ''),
title=Prettyfier.get_label(name))
if hidex: ax.set(xticklabels=[])
else:
xsym = r'\phi' if distance_distribution_name == 'levelset' else r'|x|'
ax.set(xlabel=r'$%s$ [$mm$]' % xsym)
plot(axes[0], 'mvd', label=True, scalefactor=1.e6)
fmt_axis_labels(axes[0], 'mvd')
axes[0].legend()
plot(axes[1], 'phi_vessels', scalefactor=1.)
fmt_axis_labels(axes[1], 'phi_vessels', scalefactor=1)
plot(axes[2], 'shearforce')
fmt_axis_labels(
axes[2],
'shearforce',
)
plot(axes[3], 'velocity')
fmt_axis_labels(
axes[3],
'velocity',
)
plot(axes[4], 'radius')
fmt_axis_labels(axes[4], 'radius', hidex=False)
plot(axes[5], 'flow', scalefactor=1)
fmt_axis_labels(axes[5], 'flow', scalefactor=1, hidex=False)
#plot(axes[5], 'maturation', scalefactor = 1)
#fmt_axis_labels(axes[5], 'maturation', scalefactor = 1, hidex=False)
pdfwriter.savefig(fig, postfix='_radial')
linewidth=2.)
# full o2, tumor
q = mydata(theta=1.)
plot.plot(args['ncells'],
q,
'r',
label=r'$\theta = 1$ (tumor)',
linewidth=2.)
# varying o2
for o2 in o2values:
q = mydata(theta=1., o2=o2)
plot.plot(args['ncells'], q, 'r--')
plot.text(0.1,
mydata(o2=o2, ncells=0.1)[0] + 0.001,
r'$O_2 = %s$' % f2s(o2))
# finished
plot.legend()
levels = [-1. / 48, -1. / 238, 0., 1. / 238, 1. / 48, 1. / 24]
levellabels = ['-1/48', '-1/240', '0', '1/240', '1/48', '1/24']
plot.set(yticks=levels, yticklabels=levellabels)
pdfpages.savefig(fig)
# plot vs o2
fig = pyplot.figure()
plot = fig.add_subplot(
111,
from os.path import basename, splitext
import h5py
import os, sys
import numpy as np
import extensions # for hdf5 support in np.asarray
import krebsutils
import myutils
import plotBulkTissue2d
import plotBulkTissue
import matplotlib
import matplotlib.pyplot as pyplot
import mpl_utils
f2l = lambda x: myutils.f2s(x, prec=2, latex=True)
def analyzeO2_with_tumor(f):
dataman = myutils.DataManager(100,
[plotBulkTissue2d.DataTumorTissueSingle()])
resultfile = plotBulkTissue2d.ResultFile(f, dataman)
group = resultfile.groupnames[-1]
ld = resultfile.obtain_data('ld')
print group
arterial_reference = 12.
