def plot_freq_errors(ax3, profilesAxaxis, abs_errors_profiles,
meanabserrorsprofiles, maxabserrorprofiles):
"""
"""
ylim = 0.67
boxwidth = 2.2
font = {
'size': 14,
}
ax3.plot(profilesAxaxis,
meanabserrorsprofiles,
linestyle='',
marker='.',
color='b')
# ax3.plot(profilesAxaxis, maxabserrorprofiles, 'r--') # dotted line for min and max
# plot boxlabels
boxypos = np.asarray(maxabserrorprofiles) + 0.015
boxxpos = np.asarray(profilesAxaxis) - boxwidth / 2.2
for i, boxlabel in enumerate(boxlabelsA):
ax3.text(boxxpos[i], boxypos[i], boxlabel, fontdict=font)
bp = ax3.boxplot(
abs_errors_profiles,
positions=profilesAxaxis,
widths=boxwidth,
)
_initaxis([ax3],
legend='upper right',
xlabel='frequency of reference pattern (bpm)',
ylabel='absolute error (mm)')
major_ticks = np.arange(40, 100, 10)
ax3.set_ylim((0, ylim))
ax3.set_xlim(40, 100)
ax3.set_xticks(major_ticks)
plt.setp(bp['boxes'], color='k')
plt.setp(bp['whiskers'], color='k', linestyle='-')
plt.setp(bp['fliers'], color='k', marker='+')
plt.setp(bp['medians'], color='k')
return bp
def plot_ampl_errors(ax1, profilesBxaxis, abs_errors_profiles,
meanabserrorsprofiles, maxabserrorprofiles):
"""
"""
xlim = 1.45
ylim = 0.67
boxwidth = 0.06
font = {
'size': 14,
}
#plot line x=y
ax1.plot([0, xlim], [0, xlim], 'k--')
ax1.plot([0, xlim], [0, xlim / 2], 'k-.')
#plot text x=y
ax1.text(0.78, 0.5, 'y=0.5x', fontdict=font)
ax1.text(0.39, 0.5, 'y=x', fontdict=font)
ax1.plot(profilesBxaxis,
meanabserrorsprofiles,
linestyle='',
marker='.',
color='b')
# ax1.plot(profilesBxaxis, maxabserrorprofiles, 'r--') # dotted line for min and max
# meanvalues = np.asarray(meanabserrorsprofiles)
# stdvalues = np.asarray(stdabserrorprofiles)
# ax1.fill_between(profilesBxaxis, meanvalues-stdvalues,
# meanvalues+stdvalues, color='r', alpha=0.2)
# plot boxlabels
boxypos = np.asarray(maxabserrorprofiles) + 0.015
boxxpos = np.asarray(profilesBxaxis) - boxwidth / 2.2
for i, boxlabel in enumerate(boxlabelsB):
ax1.text(boxxpos[i], boxypos[i], boxlabel, fontdict=font)
bp = ax1.boxplot(
abs_errors_profiles,
positions=profilesBxaxis,
widths=boxwidth,
patch_artist=True, # fill with color)
) # dict
# fill with colors
gray = (211 / 255, 211 / 255, 211 / 255, 0.5) # with alpha
colors = ['white', 'white', 'white', 'white', gray, gray, 'white']
for patch, color in zip(bp['boxes'], colors):
patch.set_facecolor(color)
_initaxis([ax1],
legend='upper right',
xlabel='amplitude of reference pattern (mm)',
ylabel='absolute error (mm)')
major_ticks = np.arange(0, xlim, 0.2)
ax1.set_ylim((0, ylim))
ax1.set_xlim(-0.04, xlim)
ax1.set_xticks(major_ticks)
# plt.setp(bp['boxes'], color='k')
plt.setp(bp['whiskers'], color='k', linestyle='-')
plt.setp(bp['fliers'], color='k', marker='+')
# Tweak spacing to prevent clipping of ylabel
# plt.subplots_adjust(left=0.15)
return bp
def show_periods_cam123(ttperiodsC1,
ttperiodsC2,
ttperiodsC3,
pperiodsC1,
pperiodsC2,
pperiodsC3,
shiftt0=True,
ax=None):
colors = ['#d7191c', '#fdae61', '#2c7bb6'
] # http://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
for p, ttperiod in enumerate(ttperiodsC1):
if shiftt0 == True:
offsett = ttperiod[0] # start at t=0
else:
offsett = 0 # no shift
if p == 0:
ax.plot(ttperiod - offsett,
pperiodsC1[p],
'.-',
color=colors[0],
alpha=0.5,
label='camera reference 1')
else:
ax.plot(ttperiod - offsett,
pperiodsC1[p],
'.-',
color=colors[0],
alpha=0.5)
for p, ttperiod in enumerate(ttperiodsC2):
if shiftt0 == True:
offsett = ttperiod[0] # start at t=0
else:
offsett = 0 # no shift
if p == 0:
ax.plot(ttperiod - offsett,
pperiodsC2[p],
'.-',
color=colors[1],
alpha=0.5,
label='camera reference 2')
else:
ax.plot(ttperiod - offsett,
pperiodsC2[p],
'.-',
color=colors[1],
alpha=0.5)
for p, ttperiod in enumerate(ttperiodsC3):
if shiftt0 == True:
offsett = ttperiod[0] # start at t=0
else:
offsett = 0 # no shift
if p == 0:
ax.plot(ttperiod - offsett,
pperiodsC3[p],
'.-',
alpha=0.5,
color=colors[2],
label='camera reference 3')
else:
ax.plot(ttperiod - offsett,
pperiodsC3[p],
'.-',
alpha=0.5,
color=colors[2])
ax.set_ylim((-0.02, ylim))
ax.set_xlim(-0.1, max((ttperiod - offsett)) + 0.1)
_initaxis([ax],
legend='upper right',
xlabel='time (s)',
ylabel='position (mm)')
f1 = plt.figure(num=1, figsize=(7.6, 5))
ax1 = f1.add_subplot(111)
xlim = 1.45
ylim = 0.65
ax1.plot(profilesBxaxis, meanabserrorsprofiles, linestyle='r-', marker='.', color='b')
# ax1.plot(profilesBxaxis, maxabserrorprofiles, 'r--') # dotted line for min and max
# meanvalues = np.asarray(meanabserrorsprofiles)
# stdvalues = np.asarray(stdabserrorprofiles)
# ax1.fill_between(profilesBxaxis, meanvalues-stdvalues,
# meanvalues+stdvalues, color='r', alpha=0.2)
bp = plt.boxplot(abs_errors_profiles, positions=profilesBxaxis, widths=0.06)
_initaxis([ax1], legend='upper right', xlabel='amplitude of pattern (mm)',
ylabel='absolute error (mm)')
major_ticks = np.arange(0, xlim, 0.2)
ax1.set_ylim((0, ylim))
ax1.set_xlim(-0.04,xlim)
ax1.set_xticks(major_ticks)
plt.setp(bp['boxes'], color='k')
plt.setp(bp['whiskers'], color='k', linestyle='-')
plt.setp(bp['fliers'], color='k', marker='+')
# save
if savefig:
f1.savefig(os.path.join(dirsave, 'abserrorgraphampl.pdf'), papertype='a0', dpi=300)
## frequency patterns
abs_errors_profiles = read_error_cam123(exceldir, workbook, profilesA)
def show_period_cam123_with_bounds(ttperiodsC1,
ttperiodsC2,
ttperiodsC3,
pperiodsC1,
pperiodsC2,
pperiodsC3,
shiftt0=True,
fignum=3):
pperiodsC1mean, pperiodsC1std = np.mean(pperiodsC1,
axis=0), np.std(pperiodsC1, axis=0)
pperiodsC2mean, pperiodsC2std = np.mean(pperiodsC2,
axis=0), np.std(pperiodsC2, axis=0)
pperiodsC3mean, pperiodsC3std = np.mean(pperiodsC3,
axis=0), np.std(pperiodsC3, axis=0)
# pperiodsC2q25 = np.percentile(pperiodsC2, 25, axis=0)
# pperiodsC2q75 = np.percentile(pperiodsC2, 75, axis=0)
# shift to t0 = 0
if shiftt0 == True:
ttperiodsC1 = [tt - tt[0] for tt in ttperiodsC1]
ttperiodsC2 = [tt - tt[0] for tt in ttperiodsC2]
ttperiodsC3 = [tt - tt[0] for tt in ttperiodsC3]
# get mean tt per cam
ttperiodsC1mean = np.mean(ttperiodsC1, axis=0)
ttperiodsC2mean = np.mean(ttperiodsC2, axis=0)
ttperiodsC3mean = np.mean(ttperiodsC3, axis=0)
# get average of all tt periods of the 3 cams
ttperiodsC123t0 = np.concatenate((ttperiodsC1, ttperiodsC2, ttperiodsC3))
ttperiodsC123mean = np.mean(ttperiodsC123t0, axis=0)
# get average of all pp periods of the 3 cams
pperiodsC123 = np.concatenate((pperiodsC1, pperiodsC2, pperiodsC3))
pperiodsC123mean, pperiodsC123std = np.mean(pperiodsC123,
axis=0), np.std(pperiodsC123,
axis=0)
# plot
f3 = plt.figure(figsize=(18, 5.5), num=fignum)
plt.clf()
ax2 = f3.add_subplot(121)
colors = ['#d7191c', '#fdae61', '#2c7bb6'
] # http://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
ax2.plot(ttperiodsC1mean,
pperiodsC1mean,
'.-',
color=colors[0],
label='camera reference 1')
ax2.fill_between(ttperiodsC1mean,
pperiodsC1mean - pperiodsC1std,
pperiodsC1mean + pperiodsC1std,
color=colors[0],
alpha=0.2)
ax2.plot(ttperiodsC2mean,
pperiodsC2mean,
'.-',
color=colors[1],
label='camera reference 2')
ax2.fill_between(ttperiodsC2mean,
pperiodsC2mean - pperiodsC2std,
pperiodsC2mean + pperiodsC2std,
color=colors[1],
alpha=0.3)
# ax2.fill_between(ttperiodsC2mean, pperiodsC2q25, pperiodsC2q75,
# color=colors[1], alpha=0.3)
ax2.plot(ttperiodsC3mean,
pperiodsC3mean,
'.-',
color=colors[2],
label='camera reference 3')
ax2.fill_between(ttperiodsC3mean,
pperiodsC3mean - pperiodsC3std,
pperiodsC3mean + pperiodsC3std,
color=colors[2],
alpha=0.2)
_initaxis([ax2],
legend='upper right',
xlabel='time (s)',
ylabel='position (mm)')
ax2.set_ylim((0, ylim))
ax2.set_xlim(-0.1, max(ttperiodsC3mean) + 0.1)
# add plot of average with bounds
ax3 = f3.add_subplot(122)
ax3.plot(ttperiodsC123mean,
pperiodsC123mean,
'.-',
color='k',
label='camera reference mean')
ax3.fill_between(ttperiodsC123mean,
pperiodsC123mean - pperiodsC123std,
pperiodsC123mean + pperiodsC123std,
color='k',
alpha=0.2)
_initaxis([ax3],
legend='upper right',
xlabel='time (s)',
ylabel='position (mm)')
ax3.set_ylim((0, ylim))
ax3.set_xlim(-0.1, max(ttperiodsC123mean) + 0.1)
return ttperiodsC123mean, pperiodsC123mean, pperiodsC123std
def show_period_cam123bestCut_with_bounds(ttperiodmeanC1,
ttperiodmeanC2,
ttperiodmeanC3,
ttperiodmeanC123,
pperiodsC1bestCut,
pperiodsC2bestCut,
pperiodsC3bestCut,
pperiodsC123bestCut,
tt1a=None,
aa1a=None,
fignum=4,
save=True):
from stentseg.utils.aortamotionpattern import get_motion_pattern, plot_pattern_plt
# mean and std per cam
pperiodsC1mean, pperiodsC1std = np.nanmean(
pperiodsC1bestCut, axis=0), np.nanstd(pperiodsC1bestCut, axis=0)
pperiodsC2mean, pperiodsC2std = np.nanmean(
pperiodsC2bestCut, axis=0), np.nanstd(pperiodsC2bestCut, axis=0)
pperiodsC3mean, pperiodsC3std = np.nanmean(
pperiodsC3bestCut, axis=0), np.nanstd(pperiodsC3bestCut, axis=0)
# get average of all pp periods of the 3 cams
pperiodsC123mean = np.nanmean(pperiodsC123bestCut, axis=0)
pperiodsC123std = np.nanstd(pperiodsC123bestCut, axis=0)
# plot
f3 = plt.figure(figsize=(18, 5.5), num=fignum)
plt.clf()
ax2 = f3.add_subplot(121)
# repeat period
ttperiodmeanC1rep, pperiodsC1mean, pperiodsC1std = repeatCamPeriod(
ttperiodmeanC1, pperiodsC1mean, pperiodsC1std)
ttperiodmeanC2rep, pperiodsC2mean, pperiodsC2std = repeatCamPeriod(
ttperiodmeanC2, pperiodsC2mean, pperiodsC2std)
ttperiodmeanC3rep, pperiodsC3mean, pperiodsC3std = repeatCamPeriod(
ttperiodmeanC3, pperiodsC3mean, pperiodsC3std)
# # modify repeated for manual lag shift (for revision fig 2 B5)
# ttperiodmeanC1rep = ttperiodmeanC1rep[:-1]
# pperiodsC1mean = pperiodsC1mean[1:]
# pperiodsC1std = pperiodsC1std[1:]
# add input function simulator; first run gauspatterns.py
if tt1a:
plot_pattern_plt(*(tt1a, aa1a), label='input B5', ls='--', mark=False)
colors = ['#d7191c', '#fdae61', '#2c7bb6'
] # http://colorbrewer2.org/#type=diverging&scheme=RdYlBu&n=5
ax2.plot(ttperiodmeanC1rep,
pperiodsC1mean,
'.-',
color=colors[0],
label='output day 1 (camera)')
ax2.fill_between(ttperiodmeanC1rep,
pperiodsC1mean - pperiodsC1std,
pperiodsC1mean + pperiodsC1std,
color=colors[0],
alpha=0.2)
ax2.plot(ttperiodmeanC2rep,
pperiodsC2mean,
'.-',
color=colors[1],
label='output day 2 (camera)')
ax2.fill_between(ttperiodmeanC2rep,
pperiodsC2mean - pperiodsC2std,
pperiodsC2mean + pperiodsC2std,
color=colors[1],
alpha=0.3)
# ax2.fill_between(ttperiodmeanC2rep, pperiodsC2q25, pperiodsC2q75,
# color=colors[1], alpha=0.3)
ax2.plot(ttperiodmeanC3rep,
pperiodsC3mean,
'.-',
color=colors[2],
label='output day 3 (camera)')
ax2.fill_between(ttperiodmeanC3rep,
pperiodsC3mean - pperiodsC3std,
pperiodsC3mean + pperiodsC3std,
color=colors[2],
alpha=0.2)
_initaxis([ax2],
legend='upper right',
xlabel='time (s)',
ylabel='position (mm)')
ax2.set_ylim((0, ylim))
# ax2.set_xlim(-0.1,max(ttperiodmeanC3rep)+0.1)
xlim = 1.54 # 1.5, 2.1, 1.1; 1.54 for fig 2 paper revision
major_ticks = np.arange(0, xlim, 0.2)
ax2.set_xlim(-0.02, xlim)
ax2.set_xticks(major_ticks)
# add plot of average with bounds
ax3 = f3.add_subplot(122)
# add input function simulator
if tt1a:
plot_pattern_plt(*(tt1a, aa1a), label='input B5',
ls='--') # (A1: A=1.0, T=1.2)')
ttperiodmeanC123rep, pperiodsC123meanRep, pperiodsC123stdRep = repeatCamPeriod(
ttperiodmeanC123, pperiodsC123mean, pperiodsC123std, mark=True)
ax3.plot(ttperiodmeanC123rep,
pperiodsC123meanRep,
'.-',
color='k',
label='output mean (camera)')
ax3.fill_between(ttperiodmeanC123rep,
pperiodsC123meanRep - pperiodsC123stdRep,
pperiodsC123meanRep + pperiodsC123stdRep,
color='k',
alpha=0.2)
_initaxis([ax3],
legend='upper right',
xlabel='time (s)',
ylabel='position (mm)')
ax3.set_ylim((0, ylim))
# ax3.set_xlim(-0.1,max(ttperiodmeanC123rep)+0.1)
ax3.set_xlim(-0.02, xlim)
ax3.set_xticks(major_ticks)
if save:
f3.savefig(os.path.join(dirsave, 'simInputOutput.png'),
papertype='a0',
dpi=600)
return pperiodsC123mean, pperiodsC123std, pperiodsC123meanRep, pperiodsC123stdRep, ttperiodmeanC123rep
def plot_curvature_during_cycle(self,
patients=['LSPEAS_001'],
analysis=[''],
ctcode='discharge',
ring='R1',
ylim=[0, 2],
ylimRel=[-0.1, 0.1],
saveFig=False):
""" plot change in curvature during the cardiac cycle for point with max curvature
change at ring or ring quadrants at a certain time point (CT scan)
* ctcode = discharge, 1month, 6 months, ... 24months
* analysis = '' for ring or Q1, Q2, Q3, Q4
"""
self.curveAll = {}
self.curveRelAll = {}
# init figure
self.f1 = plt.figure(figsize=(11.5, 4.2))
xlabels = ['0', '10', '20', '30', '40', '50', '60', '70', '80', '90']
xrange = range(
1, 1 + len(xlabels)) # not start from x=0 to play with margin xlim
fontsize1 = self.fontsize1
fontsize2 = self.fontsize2
# init axis
factor = 1.33 # 1.36
gs = gridspec.GridSpec(1, 2, width_ratios=[1,
factor]) # plot right wider
ax1 = plt.subplot(gs[0])
plt.xticks(xrange, xlabels, fontsize=fontsize1)
ax2 = plt.subplot(gs[1])
plt.xticks(xrange, xlabels, fontsize=fontsize1)
ax1.set_ylabel('Curvature (cm$^{-1}$)',
fontsize=fontsize2) # absolute curvature values
ax1.set_ylim(ylim)
ax1.set_xlim([0.8, len(xlabels) + 0.2]) # xlim margins 0.2
ax1.set_xlabel('Phase in cardiac cycle', fontsize=fontsize2)
ax2.set_ylabel('Relative curvature change (cm$^{-1}$)',
fontsize=fontsize2) # relative dist from avgreg
ax2.set_ylim(ylimRel)
ax2.set_xlim([0.8, len(xlabels) * factor + 0.2
]) # xlim margins 0.2; # longer for legend
ax2.set_xlabel('Phase in cardiac cycle', fontsize=fontsize2)
# plot init
lw = 1
alpha = 0.9
ls = '-'
markers = ['o', 'D', '^', 'o', 's']
colorsdirections = [ # from phantom paper in vivo plots
'k',
'#d73027', # 1 red
'#fc8d59', # orange
'#91bfdb', # blue
'#4575b4' # 5
]
Qnames = ['Full ring', 'QP', 'QA', 'QL', 'QR']
Qs = ['', 'Q1', 'Q3', 'Q2', 'Q4']
for patient in patients:
for a in analysis:
# read workbook
filename = '{}_ringdynamics_{}.xlsx'.format(patient, ctcode)
workbook_stent = os.path.join(self.exceldir, filename)
wb = openpyxl.load_workbook(workbook_stent, data_only=True)
sheetname = 'Curvature{}{}'.format(a, ring)
sheet = wb.get_sheet_by_name(sheetname)
# read cuvrature of point with max change
rowstart = 12 # as in excel; during cycle
colstart = 1 # 1 = B
# read change
curvatures = sheet.rows[rowstart - 1][colstart:colstart + 10]
curvatures = [obj.value for obj in curvatures]
# convert to array
curvatures = np.asarray(curvatures)
# get curvature at mid heart cycle
avgcurvature = np.nanmean(curvatures)
# relative curvatures from avgreg
curveRel = curvatures - avgcurvature
self.curveAll['{}_{}'.format(patient, a)] = curvatures
self.curveRelAll['{}_{}'.format(patient, a)] = curveRel
# plotting
# color1 = color_per_patient(patient)
color1 = colorsdirections[Qs.index(a)]
marker = markers[Qs.index(a)]
label = Qnames[Qs.index(a)]
ax1.plot(xrange,
curvatures,
ls=ls,
lw=lw,
marker=marker,
color=color1,
label=label,
alpha=alpha)
ax2.plot(xrange,
curveRel,
ls=ls,
lw=lw,
marker=marker,
color=color1,
label=label,
alpha=alpha)
ax2.legend(loc='upper right',
fontsize=self.fontsize4,
numpoints=1,
title='Legend:')
_initaxis([ax1, ax2], axsize=fontsize1)
if saveFig:
plt.savefig(os.path.join(
self.dirsaveIm,
'plot_curvature_over_cycle_{}.png'.format(analysis)),
papertype='a0',
dpi=600)
ax0.scatter(ttPeriodStarts2 - offsett2,
np.array(peakmin2)[:, 1],
color='green')
ax0.scatter(ttPeriodPeaks2 - offsett2, np.array(peakmax2)[:, 1], color='k')
ax0.plot(time_all_cam3t0,
pos_all_cam3,
'b.-',
alpha=0.5,
label='camera reference 3')
ax0.scatter(ttPeriodStarts3 - offsett3,
np.array(peakmin3)[:, 1],
color='green')
ax0.scatter(ttPeriodPeaks3 - offsett3, np.array(peakmax3)[:, 1], color='k')
_initaxis([ax0],
legend='upper right',
xlabel='time (s)',
ylabel='position (mm)')
ax0.set_ylim((-0.02, ylim))
ax0.set_xlim(xlim)
## overlay signals with consecutive periods, smallest rmse
ax1 = f1.add_subplot(212)
numpeaks = min(len(peakmin1), len(peakmin2),
len(peakmin3)) # num peaks to analyse
peakstart = int(peakmin2[0, 0]) # cam2 as ref
peakend = int(peakmin2[numpeaks - 1, 0])
tc2, pc2 = time_all_cam2t0[peakstart:peakend +
1], pos_all_cam2[peakstart:peakend + 1]
def plot_tilt_lines(exceldir, workbook_stent, workbook_renal_cll, obs='obsMK',
ylim=[0,50], saveFig=False):
""" Plot tilt over time for all patients
uses the functions above to get data for each time point
"""
ctcodes=['D', '1M', '6M', '12M', '24M']
# data ring dimension
PPVVdistances = read_distance_peaks_and_valleys(exceldir, workbook_stent)
tiltPeaks_all = [] # all scans
tiltValleys_all = []
for ctcode in ctcodes:
try:
# data centerline renal
VR, PA, VL, PP = read_dist_to_renal(exceldir, workbook_renal_cll, ctcode, obs=obs)
# tilt
tiltPeaks, tiltValleys = tilt_peaks_valleys_centerline(PPVVdistances,
ctcode, VR, PA, VL, PP)
except TypeError: # when excel cells are Nonetype (empty/not scored)
tiltPeaks = np.empty((15, 1,)) * np.nan
tiltValleys = np.empty((15, 1,)) * np.nan
tiltPeaks_all.append(tiltPeaks)
tiltValleys_all.append(tiltValleys)
# init figure
f1 = plt.figure(num=1, figsize=(11.7, 5)) # 11.6,4.6 or 5
xlabels = ['D', '1M', '6M', '12M', '24M']
xrange = range(1,1+len(xlabels)) # not start from x=0 to play with margin xlim
# init axis
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 1.3]) # 6.4/5.2
ax1 = plt.subplot(gs[0])
plt.xticks(xrange, xlabels)
ax2 = plt.subplot(gs[1])
plt.xticks(xrange, xlabels)
# Set the font name and size for axis tick labels
fontName = "Arial" # most similar to Helvetica (not available), which Matlab uses
ax1.set_ylabel('Tilt peaks ($^\circ$)', fontsize=15, fontname=fontName)
ax2.set_ylabel('Tilt valleys ($^\circ$)', fontsize=15, fontname=fontName)
ax1.set_ylim(ylim)
ax2.set_ylim(ylim)
ax1.set_yticks(np.arange(0,ylim[1], 5)) # steps of 5
ax2.set_yticks(np.arange(0,ylim[1], 5))
ax1.set_xlim([0.8, len(xlabels)+0.2]) # xlim margins 0.2
ax2.set_xlim([0.8, len(xlabels)+1.4]) # longer for legend
# lines and colors; 12-class Paired (ring evolution paper)
colors = itertools.cycle(['#a6cee3','#1f78b4','#b2df8a','#33a02c',
'#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a','#ffff99','#b15928'])
# lStyles = ['-', '--']
markers = ['D', 'o', '^', 's', '*'] # for device size
lw = 1
alpha = 1
ls = '-'
ls2 = '--'
IDlegend =['01', '02','03', '05', '08', '09','11','15','17',
'18', '19', '20', '21', '22', '25']
ptdevicesizes = [25.5, 30.5, 30.5, 28, 28, 30.5, 28, 28, 32, 30.5, 30.5, 34,
30.5, 28, 34]
# to array
tiltPeaks_all = np.stack(tiltPeaks_all, axis=1) # 15x(5x1) array # np.stack from 1.10.0
tiltValleys_all = np.stack(tiltValleys_all, axis=1)
tiltPeaks_mean = np.nanmean(tiltPeaks_all, axis=0)
tiltValleys_mean = np.nanmean(tiltValleys_all, axis=0)
tiltPeaks_median = np.nanmedian(tiltPeaks_all, axis=0)
tiltValleys_median = np.nanmedian(tiltValleys_all, axis=0)
# plot per patient the data of all CT scans
for i, tiltPeak in enumerate(tiltPeaks_all):
patient = IDlegend[i]
devicesize = ptdevicesizes[i]
color = next(colors)
if devicesize == 25.5:
marker = markers[0]
olb = 'OLB25'
elif devicesize == 28:
marker = markers[1]
olb = 'OLB28'
elif devicesize == 30.5:
marker = markers[2]
olb = 'OLB30'
elif devicesize == 32:
marker = markers[3]
olb = 'OLB32'
else:
marker = markers[4]
olb = 'OLB34'
# plot tilt
ax1.plot(xrange, tiltPeaks_all[i], ls=ls, lw=lw, marker=marker, color=color,
label='%s:%s' % (patient[-2:], olb), alpha=alpha)
ax2.plot(xrange, tiltValleys_all[i], ls=ls, lw=lw, marker=marker, color=color,
label='%s:%s' % (patient[-2:], olb), alpha=alpha)
# plot mean (median?)
ax1.plot(xrange, tiltPeaks_median, ls=ls2, lw=2, marker='p', color='k',
label='Median', alpha=alpha)
ax2.plot(xrange, tiltValleys_median, ls=ls2, lw=2, marker='p', color='k',
label='Median', alpha=alpha)
# Set the font name and for axis tick labels
for tick in ax1.get_xticklabels():
tick.set_fontname(fontName)
for tick in ax2.get_xticklabels():
tick.set_fontname(fontName)
for tick in ax1.get_yticklabels():
tick.set_fontname(fontName)
for tick in ax2.get_yticklabels():
tick.set_fontname(fontName)
ax2.legend(loc='upper right', fontsize=10, numpoints=1)
_initaxis([ax1, ax2], axsize=14) # sets also xtick ytick fontsize
if saveFig:
plt.savefig(os.path.join(dirsaveIm,
'plot_pp_vv_tilt.png'), papertype='a0', dpi=600)
return f1, tiltPeaks_all, tiltValleys_all
'o-',
color=colors[1],
alpha=alpha2,
label='algorithm-Aquilion') #mean of ring-stent points
ax4.plot(ttCamSrep, pzMax2, '-.',
color=colors[1]) # dotted line for min and max
ax4.plot(ttCamSrep, pzMin2, '-.', color=colors[1])
ax4.fill_between(ttCamSrep,
pzMean2 - pzStd2,
pzMean2 + pzStd2,
color=colors[1],
alpha=alpha1)
_initaxis([ax4],
legend='upper right',
xlabel='time (s)',
ylabel='position (mm)',
legendtitle=profile)
major_ticksx = np.arange(0, xlim, 0.2)
major_ticksy = np.arange(0, ylim, 0.2)
ax4.set_ylim((0, ylim))
ax4.set_xlim(-0.02, xlim)
ax4.set_xticks(major_ticksx)
ax4.set_yticks(major_ticksy)
# store fig
if saveFig:
name = 'alg_cam123mean_{}.pdf'.format(sheetProfile)
f1.savefig(os.path.join(dirsave, name), papertype='a0', dpi=600)
# ============================================
ax1.plot(xrange,
ppEr[:, 1],
'--',
color=colors[1],
marker=marker,
label='reference - y')
ax1.plot(xrange,
ppEr[:, 2],
'--',
color=colors[2],
marker=marker,
label='reference - z')
_initaxis([ax1],
legend='upper right',
xlabel='phases of cardiac cycle',
ylabel='relative position (mm)',
legendtitle='Landmark {}'.format(landmarknr))
major_ticksx = np.arange(0, xlim, 10)
major_ticksy = np.arange(-1, ylim, 0.2)
#ax4.set_ylim((0, ylim))
ax1.set_xlim(-0.5, xlim)
ax1.set_xticks(major_ticksx)
ax1.set_yticks(major_ticksy)
# plot relative 3D displacement for one landmark
fignum = 2
f2 = plt.figure(figsize=(9, 5.5), num=fignum)
plt.clf()
ax1 = f2.add_subplot(111)
def plot_displacement_mean_over_time(self,
ylim=[0, 2],
saveFig=False,
showlegend=True):
""" Plot mean displacement rings for full ring and all 4 directions using the
mean of the patients
"""
# init figure
f1 = plt.figure(figsize=(10, 9)) # from 11.6 to 10 to place legend
xlabels = ['D', '1M', '6M', '12M', '24M']
xrange = range(
1, 1 + len(xlabels)) # not start from x=0 to play with margin xlim
# init axis
ax1 = f1.add_subplot(3, 2, 1)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
ax2 = f1.add_subplot(3, 2, 2)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
ax3 = f1.add_subplot(3, 2, 3)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
ax4 = f1.add_subplot(3, 2, 4)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
ax5 = f1.add_subplot(3, 2, 5)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
ax6 = f1.add_subplot(3, 2, 6)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
# ax7 = f1.add_subplot(4,2,7)
# plt.xticks(xrange, xlabels, fontsize = self.fontsize5)
# ax8 = f1.add_subplot(4,2,8)
# plt.xticks(xrange, xlabels, fontsize = self.fontsize5)
axes = [ax1, ax2, ax3, ax4, ax5, ax6] #, ax7, ax8]
yname2 = 'mm'
yname = 'Displacement'
for ax in axes:
ax.set_ylabel('{} ({})'.format(yname, yname2),
fontsize=self.fontsize6)
ax.set_ylim(ylim)
ax.set_xlim([0.8, len(xlabels) + 0.2]) # xlim margins 0.2
# set markers
markers = ['p', 'D', '^', 'o', 's']
colorsdirections = self.colorsdirections
# capsize = 8
lss = ['--', '-', '-', '-', '-']
# lw = 1
# lw2 = 1.1
# alpha = 0.7
Qnames = ['Full ring', 'QP', 'QA', 'QL', 'QR']
Qs = ['', 'Q1', 'Q3', 'Q2', 'Q4']
Rs = ['R1', 'R2']
filedir = os.path.join(self.filedir, self.folderringdynamics,
'Displacement')
def plot_displacement_line_errorbar(filedir,
name,
ax,
Q,
R,
xrange,
labelname,
ls,
marker,
color,
alpha=0.7,
capsize=8,
lw=1,
lw2=1.1):
"""
"""
filename = 'Displacement{}{}_{}_pts'.format(Q, R, name)
matdict = scipy.io.loadmat(os.path.join(filedir,
filename + '.mat'))
var = matdict[filename]
# plot
ax.plot(xrange,
np.nanmean(var, axis=0),
ls=ls,
lw=lw,
marker=marker,
color=color,
label=labelname,
alpha=alpha)
ax.errorbar(xrange,
np.nanmean(var, axis=0),
yerr=np.nanstd(var, axis=0),
fmt=None,
ecolor=color,
capsize=capsize,
elinewidth=lw2,
capthick=lw2)
# read mat files
for i, labelname in enumerate(Qnames):
R = Rs[0]
plot_displacement_line_errorbar(filedir, 'x', ax1, Qs[i], R,
xrange, labelname, lss[i],
markers[i], colorsdirections[i])
plot_displacement_line_errorbar(filedir, 'y', ax3, Qs[i], R,
xrange, labelname, lss[i],
markers[i], colorsdirections[i])
plot_displacement_line_errorbar(filedir, 'z', ax5, Qs[i], R,
xrange, labelname, lss[i],
markers[i], colorsdirections[i])
R = Rs[1]
plot_displacement_line_errorbar(filedir, 'x', ax2, Qs[i], R,
xrange, labelname, lss[i],
markers[i], colorsdirections[i])
plot_displacement_line_errorbar(filedir, 'y', ax4, Qs[i], R,
xrange, labelname, lss[i],
markers[i], colorsdirections[i])
plot_displacement_line_errorbar(filedir, 'z', ax6, Qs[i], R,
xrange, labelname, lss[i],
markers[i], colorsdirections[i])
if showlegend:
ax2.legend(loc='upper right',
fontsize=self.fontsize3,
numpoints=1,
title='Legend')
_initaxis(axes, axsize=self.fontsize5)
if saveFig:
plt.savefig(os.path.join(self.dirsaveIm,
'plot_ring_quadrants_displacement.png'),
papertype='a0',
dpi=600)
def plot_meancurvaturechange(self,
ylim=[0, 0.1],
saveFig=False,
showlegend=True):
"""
"""
# init figure
f1 = plt.figure(figsize=(10, 4.6)) # from 11.6 to 10 to place legend
xlabels = ['D', '1M', '6M', '12M', '24M']
xrange = range(
1, 1 + len(xlabels)) # not start from x=0 to play with margin xlim
# init axis
ax1 = f1.add_subplot(1, 2, 1)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
ax2 = f1.add_subplot(1, 2, 2)
plt.xticks(xrange, xlabels, fontsize=self.fontsize5)
axes = [ax1, ax2]
yname2 = 'cm$^{-1}$'
yname = 'Curvature change'
for ax in axes:
ax.set_ylabel('{} ({})'.format(yname, yname2),
fontsize=self.fontsize6)
ax.set_ylim(ylim)
ax.set_xlim([0.8, len(xlabels) + 0.2]) # xlim margins 0.2
# set markers
markers = ['p', 'D', '^', 'o', 's']
colorsdirections = self.colorsdirections
lss = ['--', '-', '-', '-', '-']
Qnames = ['Full ring', 'QP', 'QA', 'QL', 'QR']
Qs = ['', 'Q1', 'Q3', 'Q2', 'Q4']
Rs = ['R1', 'R2']
filedir = os.path.join(self.filedir, self.folderringdynamics,
'Curvature')
def plot_curvaturechange_line_errorbar(filedir,
name,
ax,
Q,
R,
xrange,
labelname,
ls,
marker,
color,
alpha=0.7,
capsize=8,
lw=1,
lw2=1.1):
"""
"""
filename = 'Curvature{}{}_{}_pts'.format(Q, R, name)
matdict = scipy.io.loadmat(os.path.join(filedir,
filename + '.mat'))
var = matdict[filename]
# plot
ax.plot(xrange,
np.nanmean(var, axis=0),
ls=ls,
lw=lw,
marker=marker,
color=color,
label=labelname,
alpha=alpha)
ax.errorbar(xrange,
np.nanmean(var, axis=0),
yerr=np.nanstd(var, axis=0),
fmt=None,
ecolor=color,
capsize=capsize,
elinewidth=lw2,
capthick=lw2)
# read mat files
for i, labelname in enumerate(Qnames):
R = Rs[0]
plot_curvaturechange_line_errorbar(filedir, 'meanchange', ax1,
Qs[i], R, xrange, labelname,
lss[i], markers[i],
colorsdirections[i])
R = Rs[1]
plot_curvaturechange_line_errorbar(filedir, 'meanchange', ax2,
Qs[i], R, xrange, labelname,
lss[i], markers[i],
colorsdirections[i])
if showlegend:
ax2.legend(loc='upper right',
fontsize=self.fontsize3,
numpoints=1,
title='Legend')
_initaxis(axes, axsize=self.fontsize5)
if saveFig:
plt.savefig(os.path.join(
self.dirsaveIm, 'plot_ring_quadrants_meanchangecurvature.png'),
papertype='a0',
dpi=600)