1 | #include <cpgplot.h> |
---|
2 | #include <iostream> |
---|
3 | #include <algorithm> |
---|
4 | #include <math.h> |
---|
5 | #include <Utils/utils.hh> |
---|
6 | |
---|
7 | template <class T> T absval(T value) |
---|
8 | { |
---|
9 | if( value > 0) return value; |
---|
10 | else return 0-value; |
---|
11 | } |
---|
12 | //-------------------------------------------------------------------- |
---|
13 | |
---|
14 | template <class T> void findMinMax(const T *array, const int size, |
---|
15 | T &min, T &max) |
---|
16 | { |
---|
17 | min = max = array[0]; |
---|
18 | for(int i=1;i<size;i++) { |
---|
19 | if(array[i]<min) min=array[i]; |
---|
20 | if(array[i]>max) max=array[i]; |
---|
21 | } |
---|
22 | } |
---|
23 | template void findMinMax<int>(const int *array, const int size, |
---|
24 | int &min, int &max); |
---|
25 | template void findMinMax<float>(const float *array, const int size, |
---|
26 | float &min, float &max); |
---|
27 | //-------------------------------------------------------------------- |
---|
28 | |
---|
29 | template <class T> float findMean(T *array, int size) |
---|
30 | { |
---|
31 | float mean = array[0]; |
---|
32 | for(int i=1;i<size;i++) mean += array[i]; |
---|
33 | mean /= float(size); |
---|
34 | return mean; |
---|
35 | } |
---|
36 | template float findMean<int>(int *array, int size); |
---|
37 | template float findMean<float>(float *array, int size); |
---|
38 | //-------------------------------------------------------------------- |
---|
39 | |
---|
40 | template <class T> float findStddev(T *array, int size) |
---|
41 | { |
---|
42 | float mean = findMean(array,size); |
---|
43 | float stddev = (array[0]-mean) * (array[0]-mean); |
---|
44 | for(int i=1;i<size;i++) stddev += (array[i]-mean)*(array[i]-mean); |
---|
45 | stddev = sqrt(stddev/float(size-1)); |
---|
46 | return stddev; |
---|
47 | } |
---|
48 | template float findStddev<int>(int *array, int size); |
---|
49 | template float findStddev<float>(float *array, int size); |
---|
50 | //-------------------------------------------------------------------- |
---|
51 | |
---|
52 | template <class T> T findMedian(T *array, int size) |
---|
53 | { |
---|
54 | // NOTE: madfm = median absolute deviation from median |
---|
55 | T *newarray = new T[size]; |
---|
56 | T median; |
---|
57 | for(int i=0;i<size;i++) newarray[i] = array[i]; |
---|
58 | sort(newarray,0,size); |
---|
59 | if((size%2)==0) median = (newarray[size/2-1]+newarray[size/2])/2; |
---|
60 | else median = newarray[size/2]; |
---|
61 | delete [] newarray; |
---|
62 | return median; |
---|
63 | } |
---|
64 | template int findMedian<int>(int *array, int size); |
---|
65 | template float findMedian<float>(float *array, int size); |
---|
66 | //-------------------------------------------------------------------- |
---|
67 | |
---|
68 | template <class T> T findMADFM(T *array, int size) |
---|
69 | { |
---|
70 | // NOTE: madfm = median absolute deviation from median |
---|
71 | T *newarray = new T[size]; |
---|
72 | T median = findMedian<T>(array,size); |
---|
73 | T madfm; |
---|
74 | for(int i=0;i<size;i++) newarray[i] = absval(array[i]-median); |
---|
75 | sort(newarray,0,size); |
---|
76 | if((size%2)==0) madfm = (newarray[size/2-1]+newarray[size/2])/2; |
---|
77 | else madfm = newarray[size/2]; |
---|
78 | delete [] newarray; |
---|
79 | return madfm; |
---|
80 | } |
---|
81 | template int findMADFM<int>(int *array, int size); |
---|
82 | template float findMADFM<float>(float *array, int size); |
---|
83 | //-------------------------------------------------------------------- |
---|
84 | |
---|
85 | template <class T> void findMedianStats(T *array, int size, |
---|
86 | T &median, T &madfm) |
---|
87 | { |
---|
88 | // NOTE: madfm = median absolute deviation from median |
---|
89 | if(size==0){ |
---|
90 | std::cerr << "Error in findMedianStats: zero sized array!\n"; |
---|
91 | return; |
---|
92 | } |
---|
93 | T *newarray = new T[size]; |
---|
94 | |
---|
95 | for(int i=0;i<size;i++) newarray[i] = array[i]; |
---|
96 | sort(newarray,0,size); |
---|
97 | // int newsize = sizeof(newarray)-1; |
---|
98 | // stable_sort(newarray,newarray+newsize); |
---|
99 | if((size%2)==0) median = (newarray[size/2-1]+newarray[size/2])/2; |
---|
100 | else median = newarray[size/2]; |
---|
101 | |
---|
102 | for(int i=0;i<size;i++) newarray[i] = absval(array[i]-median); |
---|
103 | sort(newarray,0,size); |
---|
104 | // stable_sort(newarray,newarray+newsize); |
---|
105 | if((size%2)==0) madfm = (newarray[size/2-1]+newarray[size/2])/2; |
---|
106 | else madfm = newarray[size/2]; |
---|
107 | |
---|
108 | delete [] newarray; |
---|
109 | } |
---|
110 | template void findMedianStats<int>(int *array, int size, |
---|
111 | int &median, int &madfm); |
---|
112 | template void findMedianStats<long>(long *array, int size, |
---|
113 | long &median, long &madfm); |
---|
114 | template void findMedianStats<float>(float *array, int size, |
---|
115 | float &median, float &madfm); |
---|
116 | template void findMedianStats<double>(double *array, int size, |
---|
117 | double &median, double &madfm); |
---|
118 | //-------------------------------------------------------------------- |
---|
119 | |
---|
120 | template <class T> void findMedianStats(T *array, int size, bool *isGood, |
---|
121 | T &median, T &madfm) |
---|
122 | { |
---|
123 | // NOTE: madfm = median absolute deviation from median |
---|
124 | int goodSize=0; |
---|
125 | for(int i=0;i<size;i++) if(isGood[i]) goodSize++; |
---|
126 | if(goodSize==0){ |
---|
127 | std::cerr << "Error in findMedianStats: no good values!\n"; |
---|
128 | return; |
---|
129 | } |
---|
130 | T *newarray = new T[goodSize]; |
---|
131 | for(int i=0;i<size;i++) if(isGood[i]) newarray[goodSize++] = array[i]; |
---|
132 | sort(newarray,0,goodSize); |
---|
133 | if((goodSize%2)==0) |
---|
134 | median = (newarray[goodSize/2-1]+newarray[goodSize/2])/2; |
---|
135 | else |
---|
136 | median = newarray[goodSize/2]; |
---|
137 | |
---|
138 | for(int i=0;i<goodSize;i++) newarray[i] = absval(newarray[i]-median); |
---|
139 | sort(newarray,0,goodSize); |
---|
140 | if((goodSize%2)==0) |
---|
141 | madfm = (newarray[goodSize/2-1]+newarray[goodSize/2])/2; |
---|
142 | else |
---|
143 | madfm = newarray[goodSize/2]; |
---|
144 | |
---|
145 | delete [] newarray; |
---|
146 | } |
---|
147 | template void findMedianStats<int>(int *array, int size, bool *isGood, |
---|
148 | int &median, int &madfm); |
---|
149 | template void findMedianStats<long>(long *array, int size, bool *isGood, |
---|
150 | long &median, long &madfm); |
---|
151 | template void findMedianStats<float>(float *array, int size, bool *isGood, |
---|
152 | float &median, float &madfm); |
---|
153 | template void findMedianStats<double>(double *array, int size, bool *isGood, |
---|
154 | double &median, double &madfm); |
---|
155 | //-------------------------------------------------------------------- |
---|
156 | |
---|
157 | |
---|
158 | template <class T> void findNormalStats(T *array, int size, |
---|
159 | float &mean, float &stddev) |
---|
160 | { |
---|
161 | if(size==0){ |
---|
162 | std::cerr << "Error in findNormalStats: zero sized array!\n"; |
---|
163 | return; |
---|
164 | } |
---|
165 | mean = array[0]; |
---|
166 | for(int i=1;i<size;i++){ |
---|
167 | mean += array[i]; |
---|
168 | } |
---|
169 | mean /= float(size); |
---|
170 | |
---|
171 | stddev = (array[0]-mean) * (array[0]-mean); |
---|
172 | for(int i=1;i<size;i++){ |
---|
173 | float sqdiff = (array[i]-mean)*(array[i]-mean); |
---|
174 | stddev += sqdiff; |
---|
175 | } |
---|
176 | stddev = sqrt(stddev/float(size-1)); |
---|
177 | |
---|
178 | } |
---|
179 | template void findNormalStats<int>(int *array, int size, |
---|
180 | float &mean, float &stddev); |
---|
181 | template void findNormalStats<long>(long *array, int size, |
---|
182 | float &mean, float &stddev); |
---|
183 | template void findNormalStats<float>(float *array, int size, |
---|
184 | float &mean, float &stddev); |
---|
185 | template void findNormalStats<double>(double *array, int size, |
---|
186 | float &mean, float &stddev); |
---|
187 | //-------------------------------------------------------------------- |
---|
188 | |
---|
189 | template <class T> void findNormalStats(T *array, int size, bool *isGood, |
---|
190 | float &mean, float &stddev) |
---|
191 | { |
---|
192 | int goodSize=0; |
---|
193 | for(int i=0;i<size;i++) if(isGood[i]) goodSize++; |
---|
194 | if(goodSize==0){ |
---|
195 | std::cerr << "Error in findNormalStats: no good values!\n"; |
---|
196 | return; |
---|
197 | } |
---|
198 | int start=0; |
---|
199 | while(!isGood[start]){start++;} |
---|
200 | mean = array[start]; |
---|
201 | for(int i=start+1;i<size;i++){ |
---|
202 | if(isGood[i]) mean += array[i]; |
---|
203 | } |
---|
204 | mean /= float(goodSize); |
---|
205 | |
---|
206 | stddev = (array[start]-mean) * (array[start]-mean); |
---|
207 | for(int i=1;i<size;i++){ |
---|
208 | if(isGood[i]) stddev += (array[i]-mean)*(array[i]-mean); |
---|
209 | } |
---|
210 | stddev = sqrt(stddev/float(goodSize-1)); |
---|
211 | |
---|
212 | } |
---|
213 | template void findNormalStats<int>(int *array, int size, bool *isGood, |
---|
214 | float &mean, float &stddev); |
---|
215 | template void findNormalStats<long>(long *array, int size, bool *isGood, |
---|
216 | float &mean, float &stddev); |
---|
217 | template void findNormalStats<float>(float *array, int size, bool *isGood, |
---|
218 | float &mean, float &stddev); |
---|
219 | template void findNormalStats<double>(double *array, int size, bool *isGood, |
---|
220 | float &mean, float &stddev); |
---|
221 | //-------------------------------------------------------------------- |
---|
222 | |
---|
223 | void findTrimmedHistStatsOLD(float *array, const int size, |
---|
224 | float &tmean, float &tsigma) |
---|
225 | { |
---|
226 | |
---|
227 | const int nbin = 100; |
---|
228 | float *num = new float[nbin]; |
---|
229 | bool *keep = new bool[nbin]; |
---|
230 | |
---|
231 | // HOW MANY VALUES IN EACH BIN? |
---|
232 | float min,max; |
---|
233 | findMinMax(array,size,min,max); |
---|
234 | for(int i=0; i<nbin; i++) num[i]=0; |
---|
235 | for(int i=0; i<size; i++){ |
---|
236 | float fraction = (array[i] - min) / (max - min); |
---|
237 | int bin = (int)( floor(fraction*nbin) ); |
---|
238 | if((bin>=0)&&(bin<nbin)) num[bin]++; |
---|
239 | } |
---|
240 | int binmax = 0; |
---|
241 | for(int i=1; i<nbin; i++) if(num[i]>num[binmax]) binmax = i; |
---|
242 | for(int i=0; i<nbin; i++) keep[i] = (num[i]>=num[binmax]/2); |
---|
243 | float *newarray = new float[size]; |
---|
244 | int newsize = 0; |
---|
245 | for(int i=0; i<size; i++){ |
---|
246 | float fraction = (array[i] - min) / (max - min); |
---|
247 | int bin = (int)( floor(fraction*nbin) ); |
---|
248 | if(keep[bin]) newarray[newsize++] = array[i]; |
---|
249 | } |
---|
250 | |
---|
251 | findNormalStats(newarray,newsize,tmean,tsigma); |
---|
252 | delete [] num; |
---|
253 | delete [] keep; |
---|
254 | delete [] newarray; |
---|
255 | |
---|
256 | } |
---|
257 | //-------------------------------------------------------------------- |
---|
258 | |
---|
259 | void findTrimmedHistStats2(float *array, const int size, |
---|
260 | float &tmean, float &tsigma) |
---|
261 | { |
---|
262 | |
---|
263 | const int nbin = 200; |
---|
264 | float *num = new float[nbin]; |
---|
265 | bool *keep = new bool[nbin]; |
---|
266 | |
---|
267 | // HOW MANY VALUES IN EACH BIN? |
---|
268 | float min,max; |
---|
269 | findMinMax(array,size,min,max); |
---|
270 | for(int i=0; i<nbin; i++) num[i]=0; |
---|
271 | for(int i=0; i<size; i++){ |
---|
272 | float fraction = (array[i] - min) / (max - min); |
---|
273 | int bin = (int)( floor(fraction*nbin) ); |
---|
274 | if((bin>=0)&&(bin<nbin)) num[bin]++; |
---|
275 | } |
---|
276 | int binmax = 0; |
---|
277 | for(int i=1; i<nbin; i++) if(num[i]>num[binmax]) binmax = i; |
---|
278 | for(int i=0; i<nbin; i++) keep[i] = (num[i]>=num[binmax]/2); |
---|
279 | float *newarray = new float[size]; |
---|
280 | int newsize = 0; |
---|
281 | for(int i=0; i<size; i++){ |
---|
282 | float fraction = (array[i] - min) / (max - min); |
---|
283 | int bin = (int)( floor(fraction*nbin) ); |
---|
284 | if(keep[bin]) newarray[newsize++] = array[i]; |
---|
285 | } |
---|
286 | |
---|
287 | tmean = findMean(newarray,newsize); |
---|
288 | |
---|
289 | tsigma = newsize * (max-min)/float(nbin) / |
---|
290 | (num[binmax] * erf(sqrt(M_LN2)) * sqrt(2.*M_PI)); |
---|
291 | |
---|
292 | } |
---|
293 | //-------------------------------------------------------------------- |
---|
294 | |
---|
295 | void findTrimmedHistStats(float *array, const int size, |
---|
296 | float &tmean, float &tsigma) |
---|
297 | { |
---|
298 | float datamin,datamax; |
---|
299 | findMinMax(array,size,datamin,datamax); |
---|
300 | float *sorted = new float[size]; |
---|
301 | float *cumul = new float[size]; |
---|
302 | float *angle = new float[size]; |
---|
303 | for(int i=0;i<size;i++) sorted[i] = array[i]/(datamax-datamin); |
---|
304 | sort(sorted,0,size); |
---|
305 | for(int i=0;i<size;i++) cumul[i] = (float)i/(float)size; |
---|
306 | int width =(int)( 20. * log10((float)size)); |
---|
307 | for(int i=0;i<size;i++){ |
---|
308 | int beg,end; |
---|
309 | float slope,eSlope,inter,eInter,r; |
---|
310 | if(i<width/2) beg=0; else beg=i-width/2; |
---|
311 | if(i>=size-width/2) end=size-1; else end=i+width/2; |
---|
312 | if(linear_regression(size,sorted,cumul,beg,end,slope,eSlope,inter,eInter,r)==0) |
---|
313 | angle[i] = atan( fabs(slope) ) * 180. / M_PI; |
---|
314 | else angle[i] = 90.; |
---|
315 | } |
---|
316 | |
---|
317 | // int start = 0; |
---|
318 | // while(angle[start] < 45.) start++; |
---|
319 | // int finish = size-1; |
---|
320 | // while(angle[finish] < 45.) finish--; |
---|
321 | |
---|
322 | int maxpt = 0; |
---|
323 | for(int i = 1; i<size; i++) if(angle[i]>angle[maxpt]) maxpt=i; |
---|
324 | int start = maxpt; |
---|
325 | while((start>0)&&(angle[start]>45.)) start--; |
---|
326 | int finish = maxpt; |
---|
327 | while((finish < size-1)&&(angle[finish]>45.)) finish++; |
---|
328 | |
---|
329 | // std::cerr << "npts = " << size << ", start = " << start << ", finish = " << finish << std::endl; |
---|
330 | |
---|
331 | int trimSize=0; |
---|
332 | float *newarray = new float[finish-start+1]; |
---|
333 | for(int i=0;i<finish-start+1;i++) newarray[i] = sorted[i+start]*(datamax-datamin); |
---|
334 | |
---|
335 | findNormalStats(newarray,finish-start+1,tmean,tsigma); |
---|
336 | |
---|
337 | } |
---|