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comparison m-toolbox/test/test_ao_removeVal.m @ 0:f0afece42f48

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Import.
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 % Test ao/removeVal for:
2 % - functionality
3 %
4 % M Hueller 10-03-10
5 %
6 % $Id: test_ao_removeVal.m,v 1.1 2010/03/13 06:36:20 mauro Exp $
7 %
8
9 %% Start with an AO with no "bad" data in it
10 a_orig = ao(plist(...
11 'waveform', 'sinewave', ...
12 'Nsecs', 100, ...
13 'fs', 1, ...
14 'f', 0.02, ...
15 'yunits', 'V', ...
16 't0', '2010-03-10 20:00:00.000' ...
17 )) + 2;
18 a_orig.setName('original');
19
20 %% 1) Test the case where we remove only Inf, removing data
21 value = Inf;
22 % Prepare a list of index where we add the "bad" points
23 N = randi(10, 1);
24 bad_index = randi(numel(a_orig.x), N, 1);
25
26 % Mix the AO with the "bad" values
27 y_orig = a_orig.y();
28 y_bad = y_orig;
29 y_bad(bad_index) = value;
30 a_bad = a_orig.setY(y_bad);
31 a_bad.setName('corrupted');
32
33 % Run the ao/removeVal method
34 a_clean = a_bad.removeVal(plist('value', value));
35 a_clean.setName('cleaned');
36
37 % Compare against the original:
38 % units
39 if ~eq(a_clean.yunits, a_bad.yunits)
40 disp('Warning: wrong yunits!');
41 end
42 if ~eq(a_clean.xunits, a_bad.xunits)
43 disp('Warning: wrong xunits!');
44 end
45
46 % t0
47 if ~eq(a_clean.t0, a_bad.t0)
48 disp('Warning: wrong t0!');
49 end
50
51 index = true(size(a_bad.x));
52 index(bad_index) = false;
53
54 % "good" values: x
55 x_clean = a_clean.x;
56 x_bad = a_bad.x;
57 if ~eq(x_bad(index), x_clean)
58 disp('Warning: wrong x values!');
59 end
60
61 % "good" values: y
62 y_clean = a_clean.y;
63 y_bad = a_bad.y;
64 if ~eq(y_bad(index), y_clean)
65 disp('Warning: wrong y values!');
66 end
67
68 %% 2) Test the case where we remove only NaN, removing data
69 value = NaN;
70 % Prepare a list of index where we add the "bad" points
71 N = randi(10, 1);
72 bad_index = randi(numel(a_orig.x), N, 1);
73
74 % Mix the AO with the "bad" values
75 y_orig = a_orig.y();
76 y_bad = y_orig;
77 y_bad(bad_index) = value;
78 a_bad = a_orig.setY(y_bad);
79 a_bad.setName('corrupted');
80
81 % Run the ao/removeVal method
82 a_clean = a_bad.removeVal(plist('value', value));
83 a_clean.setName('cleaned');
84
85 % Compare against the original:
86 % units
87 if ~eq(a_clean.yunits, a_bad.yunits)
88 disp('Warning: wrong yunits!');
89 end
90 if ~eq(a_clean.xunits, a_bad.xunits)
91 disp('Warning: wrong xunits!');
92 end
93
94 % t0
95 if ~eq(a_clean.t0, a_bad.t0)
96 disp('Warning: wrong t0!');
97 end
98
99 index = true(size(a_bad.x));
100 index(bad_index) = false;
101
102 % "good" values: x
103 x_clean = a_clean.x;
104 x_bad = a_bad.x;
105 if ~eq(x_bad(index), x_clean)
106 disp('Warning: wrong x values!');
107 end
108
109 % "good" values: y
110 y_clean = a_clean.y;
111 y_bad = a_bad.y;
112 if ~eq(y_bad(index), y_clean)
113 disp('Warning: wrong y values!');
114 end
115
116
117 %% 3) Test the case where we remove only 0s, removing data
118 value = 0;
119 % Prepare a list of index where we add the "bad" points
120 N = randi(10, 1);
121 bad_index = randi(numel(a_orig.x), N, 1);
122
123 % Mix the AO with the "bad" values
124 y_orig = a_orig.y();
125 y_bad = y_orig;
126 y_bad(bad_index) = value;
127 a_bad = a_orig.setY(y_bad);
128 a_bad.setName('corrupted');
129
130 % Run the ao/removeVal method
131 a_clean = a_bad.removeVal(plist('value', value));
132 a_clean.setName('cleaned');
133
134 % Compare against the original:
135 % units
136 if ~eq(a_clean.yunits, a_bad.yunits)
137 disp('Warning: wrong yunits!');
138 end
139 if ~eq(a_clean.xunits, a_bad.xunits)
140 disp('Warning: wrong xunits!');
141 end
142
143 % t0
144 if ~eq(a_clean.t0, a_bad.t0)
145 disp('Warning: wrong t0!');
146 end
147
148 index = true(size(a_bad.x));
149 index(bad_index) = false;
150
151 % "good" values: x
152 x_clean = a_clean.x;
153 x_bad = a_bad.x;
154 if ~eq(x_bad(index), x_clean)
155 disp('Warning: wrong x values!');
156 end
157
158 % "good" values: y
159 y_clean = a_clean.y;
160 y_bad = a_bad.y;
161 if ~eq(y_bad(index), y_clean)
162 disp('Warning: wrong y values!');
163 end
164
165
166 %% 4) Test the case where we remove Inf AND NaN AND 0s, removing data
167 % Prepare a list of index where we add the "bad" points
168 N_nan = randi(10, 1);
169 N_inf = randi(10, 1);
170 N_zeros = randi(10, 1);
171 nan_index = randi(numel(a_orig.x), N_nan, 1);
172 inf_index = randi(numel(a_orig.x), N_inf, 1);
173 zeros_index = randi(numel(a_orig.x), N_zeros, 1);
174
175 % Mix the AO with NaN, Inf, and 0s inside
176 y_orig = a_orig.y();
177 y_bad = y_orig;
178 y_bad(nan_index) = NaN;
179 y_bad(inf_index) = Inf;
180 y_bad(zeros_index) = 0;
181 a_bad = a_orig.setY(y_bad);
182 a_bad.setName('corrupted');
183
184 % Run the ao/removeVal method
185 value = [Inf NaN 0];
186 a_clean = a_bad.removeVal(plist('value', value));
187 a_clean.setName('cleaned');
188
189 % Compare against the original
190 % units
191 if ~eq(a_clean.yunits, a_bad.yunits)
192 disp('Warning: wrong yunits!');
193 end
194 if ~eq(a_clean.xunits, a_bad.xunits)
195 disp('Warning: wrong xunits!');
196 end
197
198 % t0
199 if ~eq(a_clean.t0, a_bad.t0)
200 disp('Warning: wrong t0!');
201 end
202
203 index = true(size(a_bad.x));
204 index(nan_index) = false;
205 index(inf_index) = false;
206 index(zeros_index) = false;
207
208 % "good" values: x
209 x_clean = a_clean.x;
210 x_bad = a_bad.x;
211 if ~eq(x_bad(index), x_clean)
212 disp('Warning: wrong x values!');
213 end
214
215 % "good" values: y
216 y_clean = a_clean.y;
217 y_bad = a_bad.y;
218 if ~eq(y_bad(index), y_clean)
219 disp('Warning: wrong y values!');
220 end
221
222 % plots the original, "bad", and "clean" data
223 iplot(a_orig, a_bad, a_clean, plist('LineStyles', {'-', '-', 'None'}, 'Markers', {'.', '.', 'o'}));
224
225 %% 5) Test the case where we remove Inf AND NaN AND 0s, interpolating data
226 % Prepare a list of index where we add the "bad" points
227 N_nan = randi(10, 1);
228 N_inf = randi(10, 1);
229 N_zeros = randi(10, 1);
230 nan_index = randi(numel(a_orig.x), N_nan, 1);
231 inf_index = randi(numel(a_orig.x), N_inf, 1);
232 zeros_index = randi(numel(a_orig.x), N_zeros, 1);
233
234 % Mix the AO with NaN, Inf, and 0s inside
235 y_orig = a_orig.y();
236 y_bad = y_orig;
237 y_bad(nan_index) = NaN;
238 y_bad(inf_index) = Inf;
239 y_bad(zeros_index) = 0;
240 a_bad = a_orig.setY(y_bad);
241 a_bad.setName('corrupted');
242
243 % Run the ao/removeVal method
244 value = [Inf NaN 0];
245 a_clean = a_bad.removeVal(plist('value', value, 'method', 'interp'));
246 a_clean.setName('cleaned');
247
248 % Compare against the original
249 % units
250 if ~eq(a_clean.yunits, a_bad.yunits)
251 disp('Warning: wrong yunits!');
252 end
253 if ~eq(a_clean.xunits, a_bad.xunits)
254 disp('Warning: wrong xunits!');
255 end
256
257 % t0
258 if ~eq(a_clean.t0, a_bad.t0)
259 disp('Warning: wrong t0!');
260 end
261
262 % plots the original, "bad", and "clean" data
263 iplot(a_orig, a_bad, a_clean, plist('LineStyles', {'-', '-', 'None'}, 'Markers', {'.', '.', 'o'}));
264