def doit(filenames):
dataman = myutils.DataManager(50, map(lambda x: x(), detailedo2Analysis.O2DataHandlers) + [ analyzeGeneral.DataTumorTissueSingle(), analyzeGeneral.DataDistanceFromCenter(), analyzeGeneral.DataBasicVessel(), analyzeGeneral.DataVesselSamples(), analyzeGeneral.DataVesselRadial(), analyzeGeneral.DataVesselGlobal(), analyzeBloodFlow.DataTumorBloodFlow()])
ensemble = EnsembleFiles(dataman, filenames,'po2/adaption/' )
out_prefix, out_suffix = myutils.splitcommonpresuffix(map(lambda s: basename(s), filenames))
output_base_filename = splitext(out_prefix+out_suffix)[0]
if ensemble.o2ConfigName:
fn_measure = 'detailedo2_%s_common.h5' % ensemble.o2ConfigName
else:
fn_measure = 'detailedo2_common.h5'
f_measure = h5files.open(fn_measure, 'a')
def cachelocation(g):
path = posixpath.join('FileCS_'+myutils.checksum(basename(g.file.filename)), g.name.strip(posixpath.sep))
return (f_measure, path)
measurementinfo = MeasurementInfo(sample_length = 30.,
cachelocation_callback = cachelocation,
distancemap_spec = 'radial')
with mpl_utils.PageWriter(output_base_filename+'.pdf', fileformats = ['pdf']) as pdfwriter:
if 0:
compare_tissue_saturation(dataman, ensemble, pdfwriter)
if 0:
#try:
# histogramGroupFinal = f_measure['combinedHistogramsFinal']
# histogramGroupInitial = f_measure['combinedHistogramsInitial']
#except KeyError:
#histogramGroupFinal = f_measure.recreate_group('combinedHistogramsFinal')
histogramGroupInitial = f_measure.recreate_group('combinedHistogramsInitial')
#ComputeHistogramsOfPo2Items(dataman, ensemble.items, measurementinfo, histogramGroupFinal)
ComputeHistogramsOfPo2Items(dataman, ensemble.items, measurementinfo, histogramGroupInitial)
#PlotHistograms(pdfwriter, histogramGroupFinal, 'tum', 'Tumor')
PlotHistograms(pdfwriter, histogramGroupInitial, 'all', 'Initial')
analyzeGeneral.DataDistanceFromCenter(),
analyzeGeneral.DataBasicVessel(),
analyzeGeneral.DataVesselSamples(),
DataPressureMvdCorrelation(200., 5, 30., cachelocation,
cachelocationEnsemble)
])
if not options.picz:
print '-----------computing sammples------------'
localSamples = dataman.obtain_data('intervascular_map_correlations',
thegroups)
globalSamples = dataman.obtain_data(
'intervascular_global_correlations', thegroups)
print '--------------plotting ---------------'
with mpl_utils.PageWriter(outputbasename + '_mvd-grad.pdf',
fileformats=['svg']) as pdfwriter:
fig, axes = pyplot.subplots(1,
2,
figsize=mpl_utils.a4size * np.asarray(
(0.8, 0.2)))
ax = axes[0]
dataPage = []
samples = localSamples
mask = np.asarray(samples['mask'])
maski = ~mask
maski[np.random.rand(len(mask)) < 0.95] = False
r = np.random.rand(len(mask))
dilution = float(options.num_samples) / len(mask)
mask[r > dilution] = False
def AnalyzeScaleVariation(filenames):
import matplotlib.pyplot as pyplot
import mpl_utils
import krebs.quantities as Q
import collections
data = []
for fn in filenames:
with h5py.File(fn, 'r+') as f:
sample = ObtainDataOfVesselFile(f)
del sample['message']
data.append(sample)
byScale = collections.defaultdict(list)
for d in data:
scale = d['scale']
byScale[scale].append(d)
for k, v in byScale.items():
byScale[k] = myutils.zipListOfDicts(v)
curves = collections.defaultdict(list)
for k, v in byScale.items():
res = ComputeSingleNumberAvgStd(v)
for name, (std, avg) in res.items():
curves[name].append((std, avg))
order = np.argsort(np.asarray(curves['scale'])[:, 0])
for k, v in curves.items():
curves[k] = np.asarray(v).transpose()[:, order]
scales = {
'mvd': (Q.um**-2).asNumber(Q.mm**-2),
'rbv': 100.,
'rbf': 60.,
}
with mpl_utils.PageWriter('vessel-calibration-analysis',
fileformats=['pdf']) as pdfwriter:
fig, axes = pyplot.subplots(3,
1,
figsize=mpl_utils.a4size *
np.asarray([0.4, 0.5]))
for scale, data in byScale.items():
bins = np.average(
data['bins'], axis=0
) # sanity check, all bins arrays have equal size, so just try to average the bin boundaries, even if it makes no real sense
x = bins
x_rbv = 0.5 * (x[1:] + x[:-1]) # bin center for rBV
# plot things
ax = axes[0]
ya = data['mvd']
ax.errorbar(x,
scales['mvd'] * np.average(ya, axis=0),
yerr=scales['mvd'] * np.std(ya, axis=0),
label=('h = %0.f' % scale))
legend = ax.legend(loc=4, fontsize='xx-small')
ax = axes[1]
scale = scales['rbv']
ya = data['rbv']
ax.errorbar(x_rbv,
scale * np.average(ya, axis=0),
yerr=scale * np.std(ya, axis=0))
ax = axes[2]
ya = data['rbf']
scale = scales['rbf']
ax.errorbar(x,
scale * np.average(ya, axis=0),
yerr=scale * np.std(ya, axis=0))
axes[0].set(ylabel='mvd [$mm^{-1}$]')
axes[1].set(ylabel='rbv [$\%$]')
axes[2].set(ylabel='rbf [$min^{-1}$]', xlabel='$|x| [\mu m]$')
pyplot.tight_layout()
pyplot.legend()
pdfwriter.savefig(fig)
fig, axes = pyplot.subplots(3,
1,
figsize=mpl_utils.a4size *
np.asarray([0.4, 0.5]))
for ax in axes:
ax.grid(linestyle=':', linewidth=0.5, color='#aaaaaa')
x = curves['scale'][0, :]
ax = axes[0]
y, yerr = scales['mvd'] * curves['mvd']
ax.errorbar(x, y, yerr=yerr)
ax = axes[1]
y, yerr = scales['rbv'] * curves['rbv']
ax.errorbar(x, y, yerr=yerr)
ax = axes[2]
y, yerr = scales['rbf'] * curves['rbf']
ax.errorbar(x, y, yerr=yerr)
axes[0].set(ylabel='mvd [$mm^{-1}$]')
axes[1].set(ylabel='rbv [$\%$]')
axes[2].set(ylabel='rbf [$min^{-1}$]', xlabel='$h [\mu m]$')
pyplot.tight_layout()
pdfwriter.savefig(fig)
if __name__ == "__main__":
filenames = sys.argv[1:-1]
pattern = sys.argv[-1]
if 0:
plotSingleRun(sys.argv[1])
else:
dataman = myutils.DataManager(100, [
DataTumorTissueSingle(),
DataVesselRadial(),
DataVesselSamples(),
DataBasicVessel(),
DataDistanceFromCenter()
])
files = [
ResultFile(h5files.open(fn, 'r+'), dataman, pattern)
for fn in filenames
]
for f in files:
fix_time(f.f)
common_filename = myutils.sanitize_posixpath(
splitext(commonprefix(filenames))[0])
with mpl_utils.PageWriter(common_filename + '.pdf',
formats=['pdf']) as pdfwriter:
PlotRadial(pdfwriter, dataman, files, 'radial')
#vesselgroups = [ f.f[f.groupnames[-1]]['vessels'] for f in files ]
#fig, ax = pyplot.subplots(1,1, figsize = (mpl_utils.a4size[0]*0.8, mpl_utils.a4size[0]*0.4))
#PlotRadiusHistogram(ax, dataman, vesselgroups, krebsutils.WITHIN_TUMOR)
#pdfwriter.savefig(fig, postfix='_radiushisto')
if 0:
printVolumina(sys.argv[2:], sys.argv[1])