suppose that we have following picture,which represent power spectral picture
my goal is following:
1.detect peak value of this power spectral picture
2.detect at which frequency it was
first of all,i have got this picture from following command
[Pxx,f]=periodogram(B,[],[],100);
plot(f,Pxx);
where B is input signal and 100 is sampling frequency,i have tried to use findpeaks command in matlab ,like this
[pxx_peaks,location]=findpeaks(Pxx);
and then find
f(location)
but it does not seem to fit to actual frequencies,so please tell me how to find frequencies from given peaks?thanks a lot of
example is following :
peaks are following :
0.417543614272817
0.389922187581014
0.381603315802419
0.601652859233616
0.396925294300794
0.369200511917405
0.477076452316346
0.792431584110476
0.612598437936600
0.564751537228850
0.940538666131177
0.600215481734847
0.985881201195063
0.950077461673118
1.24336273213410
1.84522775800633
1.73186592736729
3.46075557122590
4.93259798197976
8.47095716918618
25.2287636895831
1422.19492782494
60.8238733887811
11.3141744831953
8.65598040591953
3.92785491164888
2.51086405960291
2.27469230188760
1.90435488292485
1.25933693517960
1.52851575480462
0.933543409438383
1.21157308704582
0.821400666720535
1.28706199713640
1.19575886464231
0.736744959694641
0.986899895695809
0.758792061180657
0.542782326712391
0.704787750202814
0.998785634315287
0.522384453408780
0.602294251721841
0.525224294805813
0.624034405807298
0.498659616687732
0.656212420735658
0.866037361133916
0.624405636807668
0.435350646037440
1.22960953802959
0.891793067878849
1.06358076764171
1.34921178081181
1.02878577330537
1.93594290806582
1.14486512201656
2.01004088022982
2.24124811810385
2.15636037453584
4.81721425534142
4.87939454466131
10.5783535493504
27.0572221453758
1490.03057130613
62.3527480644562
13.6074209231800
9.85304975304259
16.3163128995995
74.1532966917877
1510.37374385290
27.7825124315786
8.66382951478539
7.72195587189507
6.06702456628919
3.35353608882459
4.90341095941571
5.07665716731356
4.47635486149688
9.79494608790444
22.9153086380666
1119.97978883924
57.0699524267842
15.2791339483160
5.36617545130941
3.90480316969632
2.58828964019220
1.16385064506181
1.55998411282069
1.14803074836796
0.468260146832541
0.467641715366303
0.698088976126660
0.504713663418641
0.375910057283262
0.331115262928959
0.204555648718379
0.182936666944843
0.293075999812128
0.272993318570981
0.280495615619829
0.148399626645134
location :
3
6
8
11
13
16
18
20
22
25
27
30
32
34
37
39
42
44
46
49
51
55
58
61
63
65
68
70
73
75
77
79
82
85
87
89
91
94
96
99
101
103
106
108
111
113
115
118
120
123
125
127
129
132
134
137
139
141
144
146
148
151
153
156
158
162
165
167
171
174
176
179
183
185
188
190
193
195
197
199
202
204
208
211
213
216
218
220
222
225
227
230
232
234
237
239
241
243
245
248
250
252
254
and f(location)
f(location)
ans =
0.3906
0.9766
1.3672
1.9531
2.3438
2.9297
3.3203
3.7109
4.1016
4.6875
5.0781
5.6641
6.0547
6.4453
7.0313
7.4219
8.0078
8.3984
8.7891
9.3750
9.7656
10.5469
11.1328
11.7188
12.1094
12.5000
13.0859
13.4766
14.0625
14.4531
14.8438
15.2344
15.8203
16.4063
16.7969
17.1875
17.5781
18.1641
18.5547
19.1406
19.5313
19.9219
20.5078
20.8984
21.4844
21.8750
22.2656
22.8516
23.2422
23.8281
24.2188
24.6094
25.0000
25.5859
25.9766
26.5625
26.9531
27.3438
27.9297
28.3203
28.7109
29.2969
29.6875
30.2734
30.6641
31.4453
32.0313
32.4219
33.2031
33.7891
34.1797
34.7656
35.5469
35.9375
36.5234
36.9141
37.5000
37.8906
38.2813
38.6719
39.2578
39.6484
40.4297
41.0156
41.4063
41.9922
42.3828
42.7734
43.1641
43.7500
44.1406
44.7266
45.1172
45.5078
46.0938
46.4844
46.8750
47.2656
47.6563
48.2422
48.6328
49.0234
49.4141
this seems to me like findpeaks does exactly what it should if used with no further parameters it
"finds local peaks in the data vector X. A local peak
is defined as a data sample which is either larger than the two
neighboring samples or is equal to Inf."
(http://www.mathworks.de/de/help/signal/ref/findpeaks.html)
since the only check this function does is testing if a point higher then is neighbours you get a lot of points in a noisy signal
you might want to limit the number of peaks findpeaks returns for example findpeaks(Pxx,'NPEAKS',n) only returns the n bigest peaks or findpeaks(X,'THRESHOLD',t) only returns the peaks which are over the threshold t
the best way might be findpeaks(X,'MINPEAKHEIGHT',m) to look for all peaks which are higher than m and determing m as a percentile of your input Pxx
Related
I am trying to find the tortuosity of a vessel in an image using function given here.
The inputs to the function are x and y coordinates of a signal/vessel. In my case, if I have a vessel of this type:
Then if I plot [x,y] coordinates of the vessel it looks like:
Then the function is able to track the vessel from one point to other and find the tortuosity index, see an example:
But If my vessel is of this type:
whose plot of [x,y] coordinates looks like:
then function track it in a zig-zag manner
My questions are:
How to make vessel track from one end to other endpoints in order to calculate tortuosity=
How to refine a vessel coordinates in the following manner, i.e define by signal pixel (see green signal)?
code:
i1=[91 92 93 94 95 96 97 88 89 90 98 99 100 101 102 103 87 104 105 106 107 84 85 86 108 83 109 110
111 112 113 114 115 116 117 117 117 117 117 117 117];
i2=[134 134 134 134 134 134 134 135 135 135 135 135 135 135 135 135 136 136 136 136 136 137 137 137
137 138 138 138 139 140 141 141 142 143 144 145 146 147 148 149 150];
figure, plot(i2',i1','*r')
[VTI, sd, mean_dm, num_inflection_pts, num_cpts, l_arch, l_cord]=vessel_tortousity_index(i2',i1',1);
if you the curves you are handling are mostly in a convex shape (and 1 pixel wide), you can call convhull to compute the convex hull of the point cloud and then remove the longest cord to obtain the curved portion, from which you can compute the tortousity, try this
tt=convhull(i2,i1);
plot(i2(tt),i1(tt))
I have a table object in Matlab with cells as shown in the snapshot:
The cells inside Land and Seamark are as below:
The class of the objects are as below:
>> class(FileData.gTruth.LabelData.Land)
ans =
'cell'
>> class(FileData.gTruth.LabelData.Land{1,1})
ans =
'double'
>> class(FileData.gTruth.LabelData)
ans =
'table'
I tried some syntax like writetable and csvwrite but i am not getting the right format of output. The reading of the Land and Seamark as shown in the figure gets jumbled(reading is columnwise and not row-wise).
I want my output to be in this order:
[1063 126 115 86] [1 169 158 147;1 104 165 66;728 105 276 43;950 113 971 40;1 107 810 23;227 133 48 15;618 131 107 20] [562 220 33 51;1736 167 26 28;532 130 18 15;393 129 23 14]
Code so far:
writetable(FileData.gTruth.LabelData,'labelled1.txt','Delimiter' , ';');
You can simply use reshape on the transpose of the two-dimensional matrices to build a new table:
Ship = [1063 126 115 86]
Land = {[1 169 158 147;1 104 165 66; 728 105 276 43; 950 113 971 40; 1 107 810 23; 227 133 48 15; 618 131 107 20]}
Seamark = {[562 220 33 51; 1736 167 26 28; 532 130 18 15; 393 129 23 14]}
t = table(Ship,Land,Seamark);
t2 = table(t.Ship,reshape(t.Land{:}.',1,[]),reshape(t.Seamark{:}.',1,[]))
writetable(t2,'mycsv.csv','WriteVariableNames',false)
The first and only row of mycsv.csv file is:
1063 126 115 86 1 169 158 147 1 104 165 66 728 105 276 43 950 113 971 40 1 107 810 23 227 133 48 15 618 131 107 20 562 220 33 51 1736 167 26 28 532 130 18 15 393 129 23 14
I used the WriteVariableNames,false Name-Value pair to indicate that the variable names are not to be included in the first row of the file.
I'm new to Keras LSTM and I building a script for prediction based on time series.
I have the data set below which describe users' power consumption in a year
User mai/13 jun/13 jul/13 ago/13 set/13 out/13 nov/13 dez/13 jan/14 fev/14 mar/14 abr/14
x1 88 99 108 90 86 79 83 75 80 69 75 61
x2 0 143 163 127 174 199 177 140 100 100 130 119
x3 51 50 41 42 51 48 58 53 53 37 47 41
x4 181 211 166 105 128 123 125 98 114 58 4 0
x5 173 187 180 195 211 212 231 188 193 168 166 0
x6 343 321 293 272 400 397 467 392 409 306 408 353
x7 215 150 161 148 153 130 165 166 198 166 150 148
x8 100 140 132 119 121 125 148 135 144 123 124 16
x9 197 193 181 161 201 161 227 154 176 148 171 172
x10 356 371 347 347 363 377 423 373 395 338 337 302
For each user, my script split the data in windows with size n, i.e., if n=6, user x1 will have the following dataset
88 99 108 90 86 79
99 108 90 86 79 83
108 90 86 79 83 75
90 86 79 83 75 80
86 79 83 75 80 69
79 83 75 80 69 75
83 75 80 69 75 61
I want my net to learn from the patterns of each window, so when I'm going to test it, based on a new window, it predicts a value considering the previous values.
Considering these variables:
dataset_length => number of months
dataset_dim => number of users
sequence_length => window size
The net I'm thinking about is:
1st layer: LSTM with sequence_length units, input_shape(None, 1) and return_sequences set true
2nd layer: LSTM with sequence_length*2 units and return_sequences set false
3rd layer: Dense with 1 unit
Is there anything wrong on my net layout? I'm testing the net with a little bit more data and not getting a very good result (high loss). What can I change for a better prediction? Do I need more layers?
Please let me know if you need some piece of code or the test results.
I'm using Octave 4.0 using Linux which is similar to Matlab
Is it possible to have a different range of numbers on a polar chart and have them show up along with the degrees also?
The normal polar plot goes from 0-359 degrees shown in black in image, I would like the range and tick values to be from 0 to 100 shown in red in the image is this possible? If so can two ranges be shown on a polar plot at the same time (0-359 and 0-100) almost like plotting 2 y axis using plotyy on the same plot?
See image below of polar plot
Here's the the numbers 0-359 and there corresponding numbers 0-100 matching up.
0 0
1 0.27855
2 0.5571
3 0.83565
4 1.11421
5 1.39276
6 1.67131
7 1.94986
8 2.22841
9 2.50696
10 2.78552
11 3.06407
12 3.34262
13 3.62117
14 3.89972
15 4.17827
16 4.45682
17 4.73538
18 5.01393
19 5.29248
20 5.57103
21 5.84958
22 6.12813
23 6.40669
24 6.68524
25 6.96379
26 7.24234
27 7.52089
28 7.79944
29 8.07799
30 8.35655
31 8.6351
32 8.91365
33 9.1922
34 9.47075
35 9.7493
36 10.0279
37 10.3064
38 10.585
39 10.8635
40 11.1421
41 11.4206
42 11.6992
43 11.9777
44 12.2563
45 12.5348
46 12.8134
47 13.0919
48 13.3705
49 13.649
50 13.9276
51 14.2061
52 14.4847
53 14.7632
54 15.0418
55 15.3203
56 15.5989
57 15.8774
58 16.156
59 16.4345
60 16.7131
61 16.9916
62 17.2702
63 17.5487
64 17.8273
65 18.1058
66 18.3844
67 18.663
68 18.9415
69 19.2201
70 19.4986
71 19.7772
72 20.0557
73 20.3343
74 20.6128
75 20.8914
76 21.1699
77 21.4485
78 21.727
79 22.0056
80 22.2841
81 22.5627
82 22.8412
83 23.1198
84 23.3983
85 23.6769
86 23.9554
87 24.234
88 24.5125
89 24.7911
90 25.0696
91 25.3482
92 25.6267
93 25.9053
94 26.1838
95 26.4624
96 26.7409
97 27.0195
98 27.2981
99 27.5766
100 27.8552
101 28.1337
102 28.4123
103 28.6908
104 28.9694
105 29.2479
106 29.5265
107 29.805
108 30.0836
109 30.3621
110 30.6407
111 30.9192
112 31.1978
113 31.4763
114 31.7549
115 32.0334
116 32.312
117 32.5905
118 32.8691
119 33.1476
120 33.4262
121 33.7047
122 33.9833
123 34.2618
124 34.5404
125 34.8189
126 35.0975
127 35.376
128 35.6546
129 35.9331
130 36.2117
131 36.4903
132 36.7688
133 37.0474
134 37.3259
135 37.6045
136 37.883
137 38.1616
138 38.4401
139 38.7187
140 38.9972
141 39.2758
142 39.5543
143 39.8329
144 40.1114
145 40.39
146 40.6685
147 40.9471
148 41.2256
149 41.5042
150 41.7827
151 42.0613
152 42.3398
153 42.6184
154 42.8969
155 43.1755
156 43.454
157 43.7326
158 44.0111
159 44.2897
160 44.5682
161 44.8468
162 45.1253
163 45.4039
164 45.6825
165 45.961
166 46.2396
167 46.5181
168 46.7967
169 47.0752
170 47.3538
171 47.6323
172 47.9109
173 48.1894
174 48.468
175 48.7465
176 49.0251
177 49.3036
178 49.5822
179 49.8607
180 50.1393
181 50.4178
182 50.6964
183 50.9749
184 51.2535
185 51.532
186 51.8106
187 52.0891
188 52.3677
189 52.6462
190 52.9248
191 53.2033
192 53.4819
193 53.7604
194 54.039
195 54.3175
196 54.5961
197 54.8747
198 55.1532
199 55.4318
200 55.7103
201 55.9889
202 56.2674
203 56.546
204 56.8245
205 57.1031
206 57.3816
207 57.6602
208 57.9387
209 58.2173
210 58.4958
211 58.7744
212 59.0529
213 59.3315
214 59.61
215 59.8886
216 60.1671
217 60.4457
218 60.7242
219 61.0028
220 61.2813
221 61.5599
222 61.8384
223 62.117
224 62.3955
225 62.6741
226 62.9526
227 63.2312
228 63.5097
229 63.7883
230 64.0669
231 64.3454
232 64.624
233 64.9025
234 65.1811
235 65.4596
236 65.7382
237 66.0167
238 66.2953
239 66.5738
240 66.8524
241 67.1309
242 67.4095
243 67.688
244 67.9666
245 68.2451
246 68.5237
247 68.8022
248 69.0808
249 69.3593
250 69.6379
251 69.9164
252 70.195
253 70.4735
254 70.7521
255 71.0306
256 71.3092
257 71.5877
258 71.8663
259 72.1448
260 72.4234
261 72.7019
262 72.9805
263 73.2591
264 73.5376
265 73.8162
266 74.0947
267 74.3733
268 74.6518
269 74.9304
270 75.2089
271 75.4875
272 75.766
273 76.0446
274 76.3231
275 76.6017
276 76.8802
277 77.1588
278 77.4373
279 77.7159
280 77.9944
281 78.273
282 78.5515
283 78.8301
284 79.1086
285 79.3872
286 79.6657
287 79.9443
288 80.2228
289 80.5014
290 80.7799
291 81.0585
292 81.337
293 81.6156
294 81.8942
295 82.1727
296 82.4513
297 82.7298
298 83.0084
299 83.2869
300 83.5655
301 83.844
302 84.1226
303 84.4011
304 84.6797
305 84.9582
306 85.2368
307 85.5153
308 85.7939
309 86.0724
310 86.351
311 86.6295
312 86.9081
313 87.1866
314 87.4652
315 87.7437
316 88.0223
317 88.3008
318 88.5794
319 88.8579
320 89.1365
321 89.415
322 89.6936
323 89.9721
324 90.2507
325 90.5292
326 90.8078
327 91.0864
328 91.3649
329 91.6435
330 91.922
331 92.2006
332 92.4791
333 92.7577
334 93.0362
335 93.3148
336 93.5933
337 93.8719
338 94.1504
339 94.429
340 94.7075
341 94.9861
342 95.2646
343 95.5432
344 95.8217
345 96.1003
346 96.3788
347 96.6574
348 96.9359
349 97.2145
350 97.493
351 97.7716
352 98.0501
353 98.3287
354 98.6072
355 98.8858
356 99.1643
357 99.4429
358 99.7214
359 100
Here's an image of the numbers 0-359 and there corresponding numbers 0-100 matching up.
Numbers matching up
The polar plot object in Octave adds the rtick and ttick properties to the parent axes which allows you to change the location of the ticks, however, there is unfortunately no tticklabel property that we can use to easily find and modify the theta tick marks.
Instead, we can plot your plot that you want the theta range to be 0 - 100 by first transforming your data to instead be 0 - 2*pi (as is expected by polar). Then after plotting both, we can use findall to locate all text objects, figure out which ones are the theta ticks, create a copy of them and modify one of the sets to appear to be 0 - 100.
% Your first plot is going to use the 0 - 2pi range for theta
theta1 = linspace(0, 2*pi, 1000);
rho1 = sin(theta1 * 5);
plot1 = polar(theta1, rho1);
hold on
% For your second plot, just transform your 0 - 100 range to be 0 - 360 instead
theta2 = 0:100;
rho2 = linspace(0, 1, numel(theta2));
modtheta2 = 2*pi * (theta2 ./ 100);
plot2 = polar(modtheta2, rho2);
% Now we need to modify all of the labels
% Find all of the original labels
labels = findall(gca, 'type', 'text');
% Figure out which ones are the radial labels. To do this we compute the distance
% from the center of the plot and find the most common distance
distances = cellfun(#(x)norm(x(1:2)), get(labels, 'Position'));
% Figure out the most common
[~, ~, b] = unique(round(distances * 100));
h = hist(b, 1:max(b));
labels = labels(b == find(h == max(h)));
% Make a copy of these labels (have to use arrayfun for 4.0.x compatibility)
blacklabels = arrayfun(#(L)copyobj(L, gca), labels);
% Shift these labels outward by 15%
arrayfun(#(x)set(x, 'Position', get(x, 'Position') * 1.15), blacklabels);
% Now set the other labels to red and change their values
set(labels, 'COlor', 'red')
for k = 1:numel(labels)
value = str2num(get(labels(k), 'String'))
% Convert the value to be between 0 and 100
newvalue = 100 * (value / 360);
set(labels(k), 'String', sprintf('%0.2f', newvalue))
end
I have extracted profile on cross-section of vessel by following code
imshow(image); % image is not shown
[cx,cy,c,xi,yi] = improfile;
Here c is the intensity along the profile and I plot them using:
x=1:length(c);
figure
plot(x,c,'o')
axis([0 45 80 150])
and it look like the one I have uploaded.
c is 33X1 intensity vector where, c=[123 126 131 138 139 141 143 143 144 141 136 126 102 96 91 100 100 113 109 96 94 90 101 107 116 123 127 130 131 132 131 129 131];
I want to detect index of c to find A (max in left side),B (max in right side),C (min in left side), D(min in rt side) and E (middle) points in the profile.
The function peakdet released in the public domain (http://www.billauer.co.il/peakdet.html) seems to do the job on the first try. You can try to play with the second parameter of the function to be more / less selective.
c=[123 126 131 138 139 141 143 143 144 141 136 126 102 96 91 100 100 113 109 96 94 90 101 107 116 123 127 130 131 132 131 129 131];
[ma mi]=peakdet(c,1);
ma =
9 144
18 113
30 132
mi =
15 91
22 90
32 129
EDIT: this question could be of interest: How to differentiate between a double peak and a single peak array in MATLAB?