-
Notifications
You must be signed in to change notification settings - Fork 2
/
rr_compareCandRB.py
338 lines (288 loc) · 12.9 KB
/
rr_compareCandRB.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
"""
Program to compare depth data from camera (C) lying in .csv-files to force data from
respiration belt (RB) lying in .csv-files as well.
Example data can be found under ....
Under "Set parameters" one can choose amongst which datasets to consider, filtering them
as desired.
A plot results of three statistical magnitudes, derived from comparing C to ground truth/RB.
They are PCC, Abs. Error and Rel. Error
created on 2022-07-03 12:33:26.424321
@author: Steffen Brinkmann
"""
## Set up environment
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import matplotlib.patches as mpatches
import numpy as np
import rr_algorithms as rra
import scipy.ndimage
import scipy.signal
## Set parameters
# modify according to what to consider during data evaluation: prob, bpmPacs, distance, method
prob = [1, 2, 4, 5, 6, 7, 8, 9] # probands: 1, 2, (3), 4, 5, 6, 7, 8, 9 exist so far
# leave out 3 as there are not all datasets for him
bpmPacs = [10, 15] # paced bpms: 10bpm, 15bpm
distance = [1, 2, 3] # distances 1m, 2m, 3m
freqs = [15] # sampling frequencies: 10fps (only RB), 15fps, 30fps
# later on automatically newly derived --> freq
# leave on 15 for example data is only of 15fps
method = ['median'] # methods: mean, median
# ONLY consider one at a time
postMedFilt = 14 # filter size for median filter on C data
# mean-method: 18 (max. 20)
# median-method: 14 (NOT HIGHER)
dec = 3 # leave on 3, decimation filter magnitude in rr_readC was kept 3 constantly
pathC = 'C:/Users/sbrin/Desktop/BA/Data/Processed/'
pathRB = 'C:/Users/sbrin/Desktop/BA/Data/Processed/'
timeScale = 1000 # 1000ms = 1s
# predefine necessary arrays
rAll = np.array([])
errAbsAll = np.array([]) # error in bpm
errRelAll = np.array([]) # error in percent
errAbs1m = np.array([])
errAbs2m = np.array([])
errAbs3m = np.array([])
errRel1m = np.array([])
errRel2m = np.array([])
errRel3m = np.array([])
r1m = np.array([])
r2m = np.array([])
r3m = np.array([])
errAbs10bpm = np.array([])
errAbs15bpm = np.array([])
errRel10bpm = np.array([])
errRel15bpm = np.array([])
r10bpm = np.array([])
r15bpm = np.array([])
probM = [3, 5, 7, 8, 9] # male probands, rest female
errAbsM = np.array([])
errAbsF = np.array([])
errRelM = np.array([])
errRelF = np.array([])
rM = np.array([])
rF = np.array([])
## get errorAbs, errorRel and r for chosen set of signals
for p in prob:
for bpmPac in bpmPacs:
for dist in distance:
for met in method:
for f in freqs:
paramSetC, _ = rra.get_parameterC(p, bpmPac, dist, met, f, dec)
paramSetRB, id = rra.get_parameterRB(p, bpmPac, dist)
filenameC = pathC+paramSetC+'.csv'
filenameRB = pathRB+paramSetRB+'.csv'
## import csv-files with data from respiration belt (RB) and camera (C) respectively
tsC, dataC = rra.read_csvC(filenameC)
tsRB, dataRB = rra.read_csvRB(filenameRB, id)
# get frequency from C data
freq = np.int16(np.round(timeScale / tsC[1]))
## align data from RB and C
# consider: recording of C and RB data started at different time instants respectively.
# Max. deviation is supposed to be <2s.
# Truncate shorter signal by 2s at start and end to make sure it lies within the longer signal
tsRBI, dataRBI = rra.interpolate(tsRB, dataRB, freq)
tsCI, dataCI = rra.interpolate(tsC, dataC, freq)
tsCal, dataCal, tsRBal, dataRBal = rra.align(tsCI, dataCI, tsRBI, dataRBI, freq)
## median filter to reduce noise in C signal
# for Raw Median Method: size=14
# for Raw Mean Method: size=18
dataCfilt = scipy.ndimage.median_filter(dataCal, size=postMedFilt)
## get RR from both signals
# approach: save ts of peaks, count number of peaks in set window (as big as possible) -->bpm
bpmC, bpmRB, errorAbs = rra.get_bpm(dataCfilt, dataRBal, bpmPac, freq)
errAbsAll = np.append(errAbsAll, errorAbs)
errorRel = errorAbs/bpmRB*100 # in %
errRelAll = np.append(errRelAll, errorRel)
## get PCC comparing C to ground truth RB
r, _, _, _ = rra.pearsonr_ci(dataCfilt, dataRBal) # caution: using filtered dataCal!
rAll = np.append(rAll, r)
## group gained statistical magnitude
if dist == 1:
errAbs1m = np.append(errAbs1m, errorAbs)
errRel1m = np.append(errRel1m, errorRel)
r1m = np.append(r1m, r)
elif dist == 2:
errAbs2m = np.append(errAbs2m, errorAbs)
errRel2m = np.append(errRel2m, errorRel)
r2m = np.append(r2m, r)
elif dist ==3:
errAbs3m = np.append(errAbs3m, errorAbs)
errRel3m = np.append(errRel3m, errorRel)
r3m = np.append(r3m, r)
if bpmPac == 10:
errAbs10bpm = np.append(errAbs10bpm, errorAbs)
errRel10bpm = np.append(errRel10bpm, errorRel)
r10bpm = np.append(r10bpm, r)
elif bpmPac == 15:
errAbs15bpm = np.append(errAbs15bpm, errorAbs)
errRel15bpm = np.append(errRel15bpm, errorRel)
r15bpm = np.append(r15bpm, r)
if p in probM:
errAbsM = np.append(errAbsM, errorAbs)
errRelM = np.append(errRelM, errorRel)
rM = np.append(rM, r)
elif p not in probM:
errAbsF = np.append(errAbsF, errorAbs)
errRelF = np.append(errRelF, errorRel)
rF = np.append(rF, r)
## Plot
# fontsizes
sSz = 10
mSz = 14
lSz = 16
plt.rc('font', size=lSz) # default text sizes
plt.rc('axes', titlesize=lSz) # fontsize of axes titles
plt.rc('axes', labelsize=lSz) # fontsize of x, y labels
plt.rc('xtick', labelsize=sSz) # fontsize of xtick labels
plt.rc('ytick', labelsize=sSz) # fontsize of ytick labels
plt.rc('figure', titlesize=lSz) # fontsize of figure title
plt.rc('figure', figsize=(10, 15)) # size of figures to be plotted big enough
# place xtick-labels (and respective data) the following way:
# M F 1m 2m 3m 10bpm 15bpm All
# Sex Distance RR
labels = ['M', '\nSex', 'F', '1m', '2m\nDistance', '3m', '10bpm', '\nRR', '15bpm', 'All']
barPos = 1
barPosSex = [barPos, barPos*2]
barPosDist = [barPos*4, barPos*5, barPos*6]
barPosRR = [barPos*8, barPos*9]
barPosAll = [barPos*11]
barWidth = 0.5
labelPos = [barPos, barPos*1.5, barPos*2,
barPos*4, barPos*5, barPos*6,
barPos*8, barPos*8.5, barPos*9,
barPos*11]
colSex = ['darkred', 'red']
colDist = ['darkgreen', 'seagreen','lightgreen']
colRR = ['darkblue', 'lightblue']
colAll = ['yellow']
# 3 subplots, one each for each magnitude, will be stacked one over another
fig = plt.figure()
gs = fig.add_gridspec(3)
(ax1, ax2, ax3) = gs.subplots()
# PCC
bpSex = ax1.boxplot([rM, rF], positions=barPosSex, widths=barWidth, patch_artist=True) # sex
bpDist = ax1.boxplot([r1m, r2m, r3m], positions=barPosDist, widths=barWidth, patch_artist=True) # dist
bpRR = ax1.boxplot([r10bpm, r15bpm], positions=barPosRR, widths=barWidth, patch_artist=True) # RR
bpAll = ax1.boxplot([rAll], positions=barPosAll, widths=barWidth, patch_artist=True) # All
ax1.yaxis.grid(True, color='lightgrey', which='major')
ax1.set(axisbelow=True,
title='PCC',
xlabel='Considered proband group',
ylabel='PCC [-]',
ylim=(0,1))
ax1.set_xticks(labelPos, labels)
ax1.tick_params(axis='x', bottom=False)
# ax1.set_xticklabels(sublabels, rotation=45)
# set facecols for all boxes, patch_artist needs to be set True!
for patch, color in zip(bpSex['boxes'], colSex):
patch.set_facecolor(color)
for patch, color in zip(bpDist['boxes'], colDist):
patch.set_facecolor(color)
for patch, color in zip(bpRR['boxes'], colRR):
patch.set_facecolor(color)
for patch, color in zip(bpAll['boxes'], colAll):
patch.set_facecolor(color)
# set median bar to be black, too
for median in bpSex['medians']:
median.set_color('black')
for median in bpDist['medians']:
median.set_color('black')
for median in bpRR['medians']:
median.set_color('black')
for median in bpAll['medians']:
median.set_color('black')
# Abs Err
bpSex = ax2.boxplot([errAbsM, errAbsF], positions=barPosSex, widths=barWidth, patch_artist=True) # sex
bpDist = ax2.boxplot([errAbs1m, errAbs2m, errAbs3m], positions=barPosDist, widths=barWidth, patch_artist=True) # dist
bpRR = ax2.boxplot([errAbs10bpm, errAbs15bpm], positions=barPosRR, widths=barWidth, patch_artist=True) # RR
bpAll = ax2.boxplot([errAbsAll], positions=barPosAll, widths=barWidth, patch_artist=True) # All
ax2.yaxis.grid(True, color='lightgrey', which='major')
ax2.set(axisbelow=True,
title='Absolute Error',
xlabel='Considered proband group',
ylabel='Abs. error [breaths/min]',)
ax2.set_xticks(labelPos, labels)
ax2.tick_params(axis='x', bottom=False)
ax2.set_ylim([0,8])
# ax2.set_xticklabels(sublabels, rotation=45)
# set facecols for all boxes, patch_artist needs to be set True!
for patch, color in zip(bpSex['boxes'], colSex):
patch.set_facecolor(color)
for patch, color in zip(bpDist['boxes'], colDist):
patch.set_facecolor(color)
for patch, color in zip(bpRR['boxes'], colRR):
patch.set_facecolor(color)
for patch, color in zip(bpAll['boxes'], colAll):
patch.set_facecolor(color)
# set median bar to be black, too
for median in bpSex['medians']:
median.set_color('black')
for median in bpDist['medians']:
median.set_color('black')
for median in bpRR['medians']:
median.set_color('black')
for median in bpAll['medians']:
median.set_color('black')
# Rel Err
bpSex = ax3.boxplot([errRelM, errRelF], positions=barPosSex, widths=barWidth, patch_artist=True) # sex
bpDist = ax3.boxplot([errRel1m, errRel2m, errRel3m], positions=barPosDist, widths=barWidth, patch_artist=True) # dist
bpRR = ax3.boxplot([errRel10bpm, errRel15bpm], positions=barPosRR, widths=barWidth, patch_artist=True) # RR
bpAll = ax3.boxplot([errRelAll], positions=barPosAll, widths=barWidth, patch_artist=True) # All
ax3.yaxis.grid(True, color='lightgrey', which='major')
ax3.set(axisbelow=True,
title='Relative Error',
xlabel='Considered proband group',
ylabel='Rel. error [%]',)
ax3.set_xticks(labelPos, labels)
ax3.tick_params(axis='x', bottom=False)
ax3.set_ylim([0,70])
# ax3.set_xticklabels(sublabels, rotation=45)
# set facecols for all boxes, patch_artist needs to be set True!
for patch, color in zip(bpSex['boxes'], colSex):
patch.set_facecolor(color)
for patch, color in zip(bpDist['boxes'], colDist):
patch.set_facecolor(color)
for patch, color in zip(bpRR['boxes'], colRR):
patch.set_facecolor(color)
for patch, color in zip(bpAll['boxes'], colAll):
patch.set_facecolor(color)
# set median bar to be black, too
for median in bpSex['medians']:
median.set_color('black')
for median in bpDist['medians']:
median.set_color('black')
for median in bpRR['medians']:
median.set_color('black')
for median in bpAll['medians']:
median.set_color('black')
# Hide x-labels and tick labels for all but bottom plot
for ax in (ax1, ax2, ax3):
ax.label_outer()
fig.tight_layout()
plt.show()
# calculate medians for my own knowledge
rMedM = np.median(rM)
rMedF = np.median(rF)
rMed1m = np.median(r1m)
rMed2m = np.median(r2m)
rMed3m = np.median(r3m)
rMed10bpm = np.median(r10bpm)
rMed15bpm = np.median(r15bpm)
rMedAll = np.median(rAll)
errAbsMedM = np.median(errAbsM)
errAbsMedF = np.median(errAbsF)
errAbsMed1m = np.median(errAbs1m)
errAbsMed2m = np.median(errAbs2m)
errAbsMed3m = np.median(errAbs3m)
errAbsMed10bpm = np.median(errAbs10bpm)
errAbsMed15bpm = np.median(errAbs15bpm)
errAbsMedAll = np.median(errAbsAll)
errRelMedM = np.median(errRelM)
errRelMedF = np.median(errRelF)
errRelMed1m = np.median(errRel1m)
errRelMed2m = np.median(errRel2m)
errRelMed3m = np.median(errRel3m)
errRelMed10bpm = np.median(errRel10bpm)
errRelMed15bpm = np.median(errRel15bpm)
errRelMedAll = np.median(errRelAll)
print('Operation terminated successfully')