test_odr.py
17.4 KB
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
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
# SciPy imports.
import numpy as np
from numpy import pi
from numpy.testing import (assert_array_almost_equal,
assert_equal, assert_warns)
from pytest import raises as assert_raises
from scipy.odr import (Data, Model, ODR, RealData, OdrStop, OdrWarning,
multilinear, exponential, unilinear, quadratic,
polynomial)
class TestODR(object):
# Bad Data for 'x'
def test_bad_data(self):
assert_raises(ValueError, Data, 2, 1)
assert_raises(ValueError, RealData, 2, 1)
# Empty Data for 'x'
def empty_data_func(self, B, x):
return B[0]*x + B[1]
def test_empty_data(self):
beta0 = [0.02, 0.0]
linear = Model(self.empty_data_func)
empty_dat = Data([], [])
assert_warns(OdrWarning, ODR,
empty_dat, linear, beta0=beta0)
empty_dat = RealData([], [])
assert_warns(OdrWarning, ODR,
empty_dat, linear, beta0=beta0)
# Explicit Example
def explicit_fcn(self, B, x):
ret = B[0] + B[1] * np.power(np.exp(B[2]*x) - 1.0, 2)
return ret
def explicit_fjd(self, B, x):
eBx = np.exp(B[2]*x)
ret = B[1] * 2.0 * (eBx-1.0) * B[2] * eBx
return ret
def explicit_fjb(self, B, x):
eBx = np.exp(B[2]*x)
res = np.vstack([np.ones(x.shape[-1]),
np.power(eBx-1.0, 2),
B[1]*2.0*(eBx-1.0)*eBx*x])
return res
def test_explicit(self):
explicit_mod = Model(
self.explicit_fcn,
fjacb=self.explicit_fjb,
fjacd=self.explicit_fjd,
meta=dict(name='Sample Explicit Model',
ref='ODRPACK UG, pg. 39'),
)
explicit_dat = Data([0.,0.,5.,7.,7.5,10.,16.,26.,30.,34.,34.5,100.],
[1265.,1263.6,1258.,1254.,1253.,1249.8,1237.,1218.,1220.6,
1213.8,1215.5,1212.])
explicit_odr = ODR(explicit_dat, explicit_mod, beta0=[1500.0, -50.0, -0.1],
ifixx=[0,0,1,1,1,1,1,1,1,1,1,0])
explicit_odr.set_job(deriv=2)
explicit_odr.set_iprint(init=0, iter=0, final=0)
out = explicit_odr.run()
assert_array_almost_equal(
out.beta,
np.array([1.2646548050648876e+03, -5.4018409956678255e+01,
-8.7849712165253724e-02]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([1.0349270280543437, 1.583997785262061, 0.0063321988657267]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[4.4949592379003039e-01, -3.7421976890364739e-01,
-8.0978217468468912e-04],
[-3.7421976890364739e-01, 1.0529686462751804e+00,
-1.9453521827942002e-03],
[-8.0978217468468912e-04, -1.9453521827942002e-03,
1.6827336938454476e-05]]),
)
# Implicit Example
def implicit_fcn(self, B, x):
return (B[2]*np.power(x[0]-B[0], 2) +
2.0*B[3]*(x[0]-B[0])*(x[1]-B[1]) +
B[4]*np.power(x[1]-B[1], 2) - 1.0)
def test_implicit(self):
implicit_mod = Model(
self.implicit_fcn,
implicit=1,
meta=dict(name='Sample Implicit Model',
ref='ODRPACK UG, pg. 49'),
)
implicit_dat = Data([
[0.5,1.2,1.6,1.86,2.12,2.36,2.44,2.36,2.06,1.74,1.34,0.9,-0.28,
-0.78,-1.36,-1.9,-2.5,-2.88,-3.18,-3.44],
[-0.12,-0.6,-1.,-1.4,-2.54,-3.36,-4.,-4.75,-5.25,-5.64,-5.97,-6.32,
-6.44,-6.44,-6.41,-6.25,-5.88,-5.5,-5.24,-4.86]],
1,
)
implicit_odr = ODR(implicit_dat, implicit_mod,
beta0=[-1.0, -3.0, 0.09, 0.02, 0.08])
out = implicit_odr.run()
assert_array_almost_equal(
out.beta,
np.array([-0.9993809167281279, -2.9310484652026476, 0.0875730502693354,
0.0162299708984738, 0.0797537982976416]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([0.1113840353364371, 0.1097673310686467, 0.0041060738314314,
0.0027500347539902, 0.0034962501532468]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[2.1089274602333052e+00, -1.9437686411979040e+00,
7.0263550868344446e-02, -4.7175267373474862e-02,
5.2515575927380355e-02],
[-1.9437686411979040e+00, 2.0481509222414456e+00,
-6.1600515853057307e-02, 4.6268827806232933e-02,
-5.8822307501391467e-02],
[7.0263550868344446e-02, -6.1600515853057307e-02,
2.8659542561579308e-03, -1.4628662260014491e-03,
1.4528860663055824e-03],
[-4.7175267373474862e-02, 4.6268827806232933e-02,
-1.4628662260014491e-03, 1.2855592885514335e-03,
-1.2692942951415293e-03],
[5.2515575927380355e-02, -5.8822307501391467e-02,
1.4528860663055824e-03, -1.2692942951415293e-03,
2.0778813389755596e-03]]),
)
# Multi-variable Example
def multi_fcn(self, B, x):
if (x < 0.0).any():
raise OdrStop
theta = pi*B[3]/2.
ctheta = np.cos(theta)
stheta = np.sin(theta)
omega = np.power(2.*pi*x*np.exp(-B[2]), B[3])
phi = np.arctan2((omega*stheta), (1.0 + omega*ctheta))
r = (B[0] - B[1]) * np.power(np.sqrt(np.power(1.0 + omega*ctheta, 2) +
np.power(omega*stheta, 2)), -B[4])
ret = np.vstack([B[1] + r*np.cos(B[4]*phi),
r*np.sin(B[4]*phi)])
return ret
def test_multi(self):
multi_mod = Model(
self.multi_fcn,
meta=dict(name='Sample Multi-Response Model',
ref='ODRPACK UG, pg. 56'),
)
multi_x = np.array([30.0, 50.0, 70.0, 100.0, 150.0, 200.0, 300.0, 500.0,
700.0, 1000.0, 1500.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0,
15000.0, 20000.0, 30000.0, 50000.0, 70000.0, 100000.0, 150000.0])
multi_y = np.array([
[4.22, 4.167, 4.132, 4.038, 4.019, 3.956, 3.884, 3.784, 3.713,
3.633, 3.54, 3.433, 3.358, 3.258, 3.193, 3.128, 3.059, 2.984,
2.934, 2.876, 2.838, 2.798, 2.759],
[0.136, 0.167, 0.188, 0.212, 0.236, 0.257, 0.276, 0.297, 0.309,
0.311, 0.314, 0.311, 0.305, 0.289, 0.277, 0.255, 0.24, 0.218,
0.202, 0.182, 0.168, 0.153, 0.139],
])
n = len(multi_x)
multi_we = np.zeros((2, 2, n), dtype=float)
multi_ifixx = np.ones(n, dtype=int)
multi_delta = np.zeros(n, dtype=float)
multi_we[0,0,:] = 559.6
multi_we[1,0,:] = multi_we[0,1,:] = -1634.0
multi_we[1,1,:] = 8397.0
for i in range(n):
if multi_x[i] < 100.0:
multi_ifixx[i] = 0
elif multi_x[i] <= 150.0:
pass # defaults are fine
elif multi_x[i] <= 1000.0:
multi_delta[i] = 25.0
elif multi_x[i] <= 10000.0:
multi_delta[i] = 560.0
elif multi_x[i] <= 100000.0:
multi_delta[i] = 9500.0
else:
multi_delta[i] = 144000.0
if multi_x[i] == 100.0 or multi_x[i] == 150.0:
multi_we[:,:,i] = 0.0
multi_dat = Data(multi_x, multi_y, wd=1e-4/np.power(multi_x, 2),
we=multi_we)
multi_odr = ODR(multi_dat, multi_mod, beta0=[4.,2.,7.,.4,.5],
delta0=multi_delta, ifixx=multi_ifixx)
multi_odr.set_job(deriv=1, del_init=1)
out = multi_odr.run()
assert_array_almost_equal(
out.beta,
np.array([4.3799880305938963, 2.4333057577497703, 8.0028845899503978,
0.5101147161764654, 0.5173902330489161]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([0.0130625231081944, 0.0130499785273277, 0.1167085962217757,
0.0132642749596149, 0.0288529201353984]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[0.0064918418231375, 0.0036159705923791, 0.0438637051470406,
-0.0058700836512467, 0.011281212888768],
[0.0036159705923791, 0.0064793789429006, 0.0517610978353126,
-0.0051181304940204, 0.0130726943624117],
[0.0438637051470406, 0.0517610978353126, 0.5182263323095322,
-0.0563083340093696, 0.1269490939468611],
[-0.0058700836512467, -0.0051181304940204, -0.0563083340093696,
0.0066939246261263, -0.0140184391377962],
[0.011281212888768, 0.0130726943624117, 0.1269490939468611,
-0.0140184391377962, 0.0316733013820852]]),
)
# Pearson's Data
# K. Pearson, Philosophical Magazine, 2, 559 (1901)
def pearson_fcn(self, B, x):
return B[0] + B[1]*x
def test_pearson(self):
p_x = np.array([0.,.9,1.8,2.6,3.3,4.4,5.2,6.1,6.5,7.4])
p_y = np.array([5.9,5.4,4.4,4.6,3.5,3.7,2.8,2.8,2.4,1.5])
p_sx = np.array([.03,.03,.04,.035,.07,.11,.13,.22,.74,1.])
p_sy = np.array([1.,.74,.5,.35,.22,.22,.12,.12,.1,.04])
p_dat = RealData(p_x, p_y, sx=p_sx, sy=p_sy)
# Reverse the data to test invariance of results
pr_dat = RealData(p_y, p_x, sx=p_sy, sy=p_sx)
p_mod = Model(self.pearson_fcn, meta=dict(name='Uni-linear Fit'))
p_odr = ODR(p_dat, p_mod, beta0=[1.,1.])
pr_odr = ODR(pr_dat, p_mod, beta0=[1.,1.])
out = p_odr.run()
assert_array_almost_equal(
out.beta,
np.array([5.4767400299231674, -0.4796082367610305]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([0.3590121690702467, 0.0706291186037444]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[0.0854275622946333, -0.0161807025443155],
[-0.0161807025443155, 0.003306337993922]]),
)
rout = pr_odr.run()
assert_array_almost_equal(
rout.beta,
np.array([11.4192022410781231, -2.0850374506165474]),
)
assert_array_almost_equal(
rout.sd_beta,
np.array([0.9820231665657161, 0.3070515616198911]),
)
assert_array_almost_equal(
rout.cov_beta,
np.array([[0.6391799462548782, -0.1955657291119177],
[-0.1955657291119177, 0.0624888159223392]]),
)
# Lorentz Peak
# The data is taken from one of the undergraduate physics labs I performed.
def lorentz(self, beta, x):
return (beta[0]*beta[1]*beta[2] / np.sqrt(np.power(x*x -
beta[2]*beta[2], 2.0) + np.power(beta[1]*x, 2.0)))
def test_lorentz(self):
l_sy = np.array([.29]*18)
l_sx = np.array([.000972971,.000948268,.000707632,.000706679,
.000706074, .000703918,.000698955,.000456856,
.000455207,.000662717,.000654619,.000652694,
.000000859202,.00106589,.00106378,.00125483, .00140818,.00241839])
l_dat = RealData(
[3.9094, 3.85945, 3.84976, 3.84716, 3.84551, 3.83964, 3.82608,
3.78847, 3.78163, 3.72558, 3.70274, 3.6973, 3.67373, 3.65982,
3.6562, 3.62498, 3.55525, 3.41886],
[652, 910.5, 984, 1000, 1007.5, 1053, 1160.5, 1409.5, 1430, 1122,
957.5, 920, 777.5, 709.5, 698, 578.5, 418.5, 275.5],
sx=l_sx,
sy=l_sy,
)
l_mod = Model(self.lorentz, meta=dict(name='Lorentz Peak'))
l_odr = ODR(l_dat, l_mod, beta0=(1000., .1, 3.8))
out = l_odr.run()
assert_array_almost_equal(
out.beta,
np.array([1.4306780846149925e+03, 1.3390509034538309e-01,
3.7798193600109009e+00]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([7.3621186811330963e-01, 3.5068899941471650e-04,
2.4451209281408992e-04]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[2.4714409064597873e-01, -6.9067261911110836e-05,
-3.1236953270424990e-05],
[-6.9067261911110836e-05, 5.6077531517333009e-08,
3.6133261832722601e-08],
[-3.1236953270424990e-05, 3.6133261832722601e-08,
2.7261220025171730e-08]]),
)
def test_ticket_1253(self):
def linear(c, x):
return c[0]*x+c[1]
c = [2.0, 3.0]
x = np.linspace(0, 10)
y = linear(c, x)
model = Model(linear)
data = Data(x, y, wd=1.0, we=1.0)
job = ODR(data, model, beta0=[1.0, 1.0])
result = job.run()
assert_equal(result.info, 2)
# Verify fix for gh-9140
def test_ifixx(self):
x1 = [-2.01, -0.99, -0.001, 1.02, 1.98]
x2 = [3.98, 1.01, 0.001, 0.998, 4.01]
fix = np.vstack((np.zeros_like(x1, dtype=int), np.ones_like(x2, dtype=int)))
data = Data(np.vstack((x1, x2)), y=1, fix=fix)
model = Model(lambda beta, x: x[1, :] - beta[0] * x[0, :]**2., implicit=True)
odr1 = ODR(data, model, beta0=np.array([1.]))
sol1 = odr1.run()
odr2 = ODR(data, model, beta0=np.array([1.]), ifixx=fix)
sol2 = odr2.run()
assert_equal(sol1.beta, sol2.beta)
# verify bugfix for #11800 in #11802
def test_ticket_11800(self):
# parameters
beta_true = np.array([1.0, 2.3, 1.1, -1.0, 1.3, 0.5])
nr_measurements = 10
std_dev_x = 0.01
x_error = np.array([[0.00063445, 0.00515731, 0.00162719, 0.01022866,
-0.01624845, 0.00482652, 0.00275988, -0.00714734, -0.00929201, -0.00687301],
[-0.00831623, -0.00821211, -0.00203459, 0.00938266, -0.00701829,
0.0032169, 0.00259194, -0.00581017, -0.0030283, 0.01014164]])
std_dev_y = 0.05
y_error = np.array([[0.05275304, 0.04519563, -0.07524086, 0.03575642,
0.04745194, 0.03806645, 0.07061601, -0.00753604, -0.02592543, -0.02394929],
[0.03632366, 0.06642266, 0.08373122, 0.03988822, -0.0092536,
-0.03750469, -0.03198903, 0.01642066, 0.01293648, -0.05627085]])
beta_solution = np.array([
2.62920235756665876536e+00, -1.26608484996299608838e+02, 1.29703572775403074502e+02,
-1.88560985401185465804e+00, 7.83834160771274923718e+01, -7.64124076838087091801e+01])
# model's function and Jacobians
def func(beta, x):
y0 = beta[0] + beta[1] * x[0, :] + beta[2] * x[1, :]
y1 = beta[3] + beta[4] * x[0, :] + beta[5] * x[1, :]
return np.vstack((y0, y1))
def df_dbeta_odr(beta, x):
nr_meas = np.shape(x)[1]
zeros = np.zeros(nr_meas)
ones = np.ones(nr_meas)
dy0 = np.array([ones, x[0, :], x[1, :], zeros, zeros, zeros])
dy1 = np.array([zeros, zeros, zeros, ones, x[0, :], x[1, :]])
return np.stack((dy0, dy1))
def df_dx_odr(beta, x):
nr_meas = np.shape(x)[1]
ones = np.ones(nr_meas)
dy0 = np.array([beta[1] * ones, beta[2] * ones])
dy1 = np.array([beta[4] * ones, beta[5] * ones])
return np.stack((dy0, dy1))
# do measurements with errors in independent and dependent variables
x0_true = np.linspace(1, 10, nr_measurements)
x1_true = np.linspace(1, 10, nr_measurements)
x_true = np.array([x0_true, x1_true])
y_true = func(beta_true, x_true)
x_meas = x_true + x_error
y_meas = y_true + y_error
# estimate model's parameters
model_f = Model(func, fjacb=df_dbeta_odr, fjacd=df_dx_odr)
data = RealData(x_meas, y_meas, sx=std_dev_x, sy=std_dev_y)
odr_obj = ODR(data, model_f, beta0=0.9 * beta_true, maxit=100)
#odr_obj.set_iprint(init=2, iter=0, iter_step=1, final=1)
odr_obj.set_job(deriv=3)
odr_out = odr_obj.run()
# check results
assert_equal(odr_out.info, 1)
assert_array_almost_equal(odr_out.beta, beta_solution)
def test_multilinear_model(self):
x = np.linspace(0.0, 5.0)
y = 10.0 + 5.0 * x
data = Data(x, y)
odr_obj = ODR(data, multilinear)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [10.0, 5.0])
def test_exponential_model(self):
x = np.linspace(0.0, 5.0)
y = -10.0 + np.exp(0.5*x)
data = Data(x, y)
odr_obj = ODR(data, exponential)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [-10.0, 0.5])
def test_polynomial_model(self):
x = np.linspace(0.0, 5.0)
y = 1.0 + 2.0 * x + 3.0 * x ** 2 + 4.0 * x ** 3
poly_model = polynomial(3)
data = Data(x, y)
odr_obj = ODR(data, poly_model)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [1.0, 2.0, 3.0, 4.0])
def test_unilinear_model(self):
x = np.linspace(0.0, 5.0)
y = 1.0 * x + 2.0
data = Data(x, y)
odr_obj = ODR(data, unilinear)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [1.0, 2.0])
def test_quadratic_model(self):
x = np.linspace(0.0, 5.0)
y = 1.0 * x ** 2 + 2.0 * x + 3.0
data = Data(x, y)
odr_obj = ODR(data, quadratic)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [1.0, 2.0, 3.0])