parser.add_argument('--field', '-f', help='Field to plot', default='PO2')
parser.add_argument('--probeName', '-p', help='Name of the probe to plot')
parser.add_argument('--from_time',
type=float,
help='Time from which to plot results',
default=0.0)
figOptions = FigureOptions(parser)
args = parser.parse_args()
fieldName = args.field
probeName = args.probeName
from_time = args.from_time
figOptions.parseOptions(args)
figOptions.applyOptions()
probes = loadProbes('domain', probeName, fieldName)
EAT_dict = extractEATs('domain', probes, 0)
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
plt.sca(ax1) # set current axes
plotType = 'minMaxTissue'
# plotType = 'minMax'
plotProbeMinMax(EAT_dict, None, from_time, plotType)
ax1.set_xlim([0.15, 0.6])
plt.sca(ax2) # set current axes
fileName = 'flow_hematocrit_EAT.csv'
csv_dict = readPO2CSV(fileName, delimiter=',')
plotAveragedBar(csv_dict)
figOptions.adjustAxes()
figOptions.setGrid()
# convert to numpy arrays
for key in return_dict.keys():
return_dict[key] = np.asarray(return_dict[key])
return return_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--field', '-f', help='Field to plot', default='PO2')
parser.add_argument('--probeName',
'-p',
help='Name of the probe directory',
default='probeMidstreamPO2')
parser.add_argument('--from_time',
type=float,
help='Time from which to plot EATs',
default=0.0)
args = parser.parse_args()
fieldName = args.field
probeName = args.probeName
from_time = args.from_time
probes = loadProbes('.', probeName, fieldName)
minMaxDict = extractProbeMinMax('.', probes, 2, from_time)
print 'Averaged minimum PO2: %g' % np.mean(minMaxDict['PO2_min'])
print 'Averaged maximum PO2: %g' % np.mean(minMaxDict['PO2_max'])
figOptions = FigureOptions(parser)
args = parser.parse_args()
time = args.time
from_time = args.from_time
figOptions.parseOptions(args)
figOptions.applyOptions()
f, (ax1, ax2) = plt.subplots(1,2, sharey=True)
plt.sca(ax1) # set current axes
plotPO2ProfileEulerian('.', time)
plt.sca(ax2) # set current axes
probeNames = probeUtils.probeNames('.', suffix='PO2')
fieldName = 'PO2'
probes = [loadProbes('.', probeName, fieldName) for probeName in probeNames]
EAT_dicts = [extractEATs('.', probe, 0) for probe in probes]
plotCompareProbes(EAT_dicts, probes, from_time)
ax2.set_ylabel('')
figOptions.adjustAxes()
figOptions.setGrid()
# put (a) and (b) at the top left corner of each plot
ax1.annotate(r'$(a)$', xy=(0.03, 0.91), xycoords='axes fraction')
ax2.annotate(r'$(b)$', xy=(0.03, 0.91), xycoords='axes fraction')
plotName = 'PO2XProfilesAndCompareProbes'
figOptions.saveFig(plotName)