forked from scikit-hep/probfit
-
Notifications
You must be signed in to change notification settings - Fork 0
/
testfunc.py
301 lines (246 loc) · 10.3 KB
/
testfunc.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
from math import log
import numpy as np
from nose.tools import assert_equal, assert_almost_equal
from iminuit import describe
from probfit import pdf
from probfit._libstat import xlogyx, wlogyx, csum, integrate1d, _vector_apply
from probfit.functor import construct_arg, fast_tuple_equal
from probfit.funcutil import merge_func_code
def f(x, y, z):
return x + y + z
def f2(x, z, a):
return x + z + a
def g(x, a, b):
return x + a + b
def h(x, c, d):
return x + c + d
def k_1(y, z):
return y + z
def k_2(i, j):
return i + j
def iterable_equal(x, y):
assert_equal(list(x), list(y))
# cpdef double doublegaussian(double x, double mean,
# double sigma_L, double sigma_R)
def test_doublegaussian():
assert_equal(
tuple(describe(pdf.doublegaussian)), ('x', 'mean', 'sigma_L', 'sigma_R'))
assert_almost_equal(pdf.doublegaussian(0., 0., 1., 2.), 1.)
assert_almost_equal(pdf.doublegaussian(-1., 0., 1., 2.), 0.6065306597126334)
assert_almost_equal(pdf.doublegaussian(1., 0., 1., 2.), 0.8824969025845955)
# cpdef double ugaussian(double x, double mean, double sigma)
def test_ugaussian():
assert_equal(tuple(describe(pdf.ugaussian)), ('x', 'mean', 'sigma'))
assert_almost_equal(pdf.ugaussian(0, 0, 1), 1.)
assert_almost_equal(pdf.ugaussian(-1, 0, 1), 0.6065306597126334)
assert_almost_equal(pdf.ugaussian(1, 0, 1), 0.6065306597126334)
# cpdef double gaussian(double x, double mean, double sigma)
def test_gaussian():
assert_equal(tuple(describe(pdf.gaussian)), ('x', 'mean', 'sigma'))
assert_almost_equal(pdf.gaussian(0, 0, 1), 0.3989422804014327)
assert_almost_equal(pdf.gaussian(-1, 0, 1), 0.24197072451914337)
assert_almost_equal(pdf.gaussian(1, 0, 1), 0.24197072451914337)
# cpdef double crystalball(double x,double alpha,double n,double mean,double sigma)
def test_crystalball():
assert_equal(tuple(describe(pdf.crystalball)),
('x', 'alpha', 'n', 'mean', 'sigma'))
assert_almost_equal(pdf.crystalball(10, 1, 2, 10, 2), 1.)
assert_almost_equal(pdf.crystalball(11, 1, 2, 10, 2), 0.8824969025845955)
assert_almost_equal(pdf.crystalball(12, 1, 2, 10, 2), 0.6065306597126334)
assert_almost_equal(pdf.crystalball(14, 1, 2, 10, 2), 0.1353352832366127)
assert_almost_equal(pdf.crystalball(6, 1, 2, 10, 2), 0.26956918209450376)
# cpdef double argus(double x, double c, double chi, double p)
def test_argus():
assert_equal(tuple(describe(pdf.argus)), ('x', 'c', 'chi', 'p'))
assert_almost_equal(pdf.argus(6., 10, 2, 3), 0.004373148605400128)
assert_almost_equal(pdf.argus(10., 10, 2, 3), 0.)
assert_almost_equal(pdf.argus(8., 10, 2, 3), 0.0018167930603254737)
# cpdef double cruijff(double x, double m_0, double sigma_L, double sigma_R, double alpha_L, double alpha_R)
def test_cruijff():
iterable_equal(tuple(describe(pdf.cruijff)),
('x', 'm_0', 'sigma_L', 'sigma_R', 'alpha_L', 'alpha_R'))
val = pdf.cruijff(0, 0, 1., 2., 1., 2.)
assert_almost_equal(val, 1.)
vl = pdf.cruijff(0, 1, 1., 1., 2., 2.)
vr = pdf.cruijff(2, 1, 1., 1., 2., 2.)
assert_almost_equal(vl, vr, msg='symmetric test')
assert_almost_equal(vl, 0.7788007830714)
assert_almost_equal(vr, 0.7788007830714)
# cpdef double linear(double x, double m, double c)
def test_linear():
assert_equal(describe(pdf.linear), ['x', 'm', 'c'])
assert_almost_equal(pdf.linear(1, 2, 3), 5)
assert(hasattr(pdf.linear, 'integrate'))
integral = pdf.linear.integrate((0., 1.), 1, 1, 1)
assert_equal(integral, 1.5)
# cpdef double poly2(double x, double a, double b, double c)
def test_poly2():
assert_equal(describe(pdf.poly2), ['x', 'a', 'b', 'c'])
assert_almost_equal(pdf.poly2(2, 3, 4, 5), 25)
# cpdef double poly3(double x, double a, double b, double c, double d)
def test_poly3():
assert_equal(describe(pdf.poly3), ['x', 'a', 'b', 'c', 'd'])
assert_almost_equal(pdf.poly3(2, 3, 4, 5, 6), 56.)
def test_polynomial():
p = pdf.Polynomial(1)
assert_equal(describe(p), ['x', 'c_0', 'c_1'])
assert_equal(p(2, 2, 1), 4)
integral = p.integrate((0, 1), 1, 2, 1)
assert_equal(integral, 2.5)
p = pdf.Polynomial(2)
assert_equal(describe(p), ['x', 'c_0', 'c_1', 'c_2'])
assert_equal(p(2, 3, 4, 5), 31)
integral = p.integrate((2, 10), 10, 1, 2, 3)
analytical = 8 + 2 / 2.*(10 ** 2 - 2 ** 2) + 3 / 3.*(10 ** 3 - 2 ** 3)
assert_equal(integral, analytical)
# cpdef double novosibirsk(double x, double width, double peak, double tail)
def test_novosibirsk():
assert_equal(describe(pdf.novosibirsk), ['x', 'width', 'peak', 'tail'])
assert_almost_equal(pdf.novosibirsk(3, 2, 3, 4), 1.1253517471925912e-07)
def test_rtv_breitwigner():
assert_equal(describe(pdf.rtv_breitwigner), ['x', 'm', 'gamma'])
assert_almost_equal(pdf.rtv_breitwigner(1, 1, 1.), 0.8194496535636714)
assert_almost_equal(pdf.rtv_breitwigner(1, 1, 2.), 0.5595531041435416)
assert_almost_equal(pdf.rtv_breitwigner(1, 2, 3.), 0.2585302502852219)
def test_cauchy():
assert_equal(describe(pdf.cauchy), ['x', 'm', 'gamma'])
assert_almost_equal(pdf.cauchy(1, 1, 1.), 0.3183098861837907)
assert_almost_equal(pdf.cauchy(1, 1, 2.), 0.15915494309189535)
assert_almost_equal(pdf.cauchy(1, 2, 4.), 0.07489644380795074)
def test_HistogramPdf():
be = np.array([0, 1, 3, 4], dtype=float)
hy = np.array([10, 30, 50], dtype=float)
norm = float((hy * np.diff(be)).sum())
f = pdf.HistogramPdf(hy, be)
assert_almost_equal(f(0.5), 10.0 / norm)
assert_almost_equal(f(1.2), 30.0 / norm)
assert_almost_equal(f(2.9), 30.0 / norm)
assert_almost_equal(f(3.6), 50.0 / norm)
assert(hasattr(f, 'integrate'))
integral = f.integrate((0, 4))
assert_almost_equal(integral, 1.0)
integral = f.integrate((0.5, 3.4))
assert_almost_equal(integral, (10 * 0.5 + 30 * 2 + 50 * 0.4) / norm)
integral = f.integrate((1.2, 4.5))
assert_almost_equal(integral, (30 * 1.8 + 50 * 1) / norm)
def test__vector_apply():
def f(x, y):
return x * x + y
y = 10
a = np.array([1., 2., 3.])
expected = [f(x, y) for x in a]
va = _vector_apply(f, a, tuple([y]))
for i in range(len(a)):
assert_almost_equal(va[i], expected[i])
def test_integrate1d():
def f(x, y):
return x * x + y
def intf(x, y):
return x * x * x / 3. + y * x
bound = (-2., 1.)
y = 3.
integral = integrate1d(f, bound, 1000, tuple([y]))
analytic = intf(bound[1], y) - intf(bound[0], y)
assert_almost_equal(integral, analytic)
def test_integrate1d_analytic():
class temp:
def __call__(self, x, m , c):
return m * x ** 2 + c
def integrate(self, bound, nint, m, c):
a, b = bound
return b - a # (wrong on purpose)
bound = (0., 10.)
f = temp()
integral = integrate1d(f, bound, 10, (2., 3.))
assert_equal(integral, bound[1] - bound[0])
def test_csum():
x = np.array([1, 2, 3], dtype=np.double)
s = csum(x)
assert_almost_equal(s, 6.)
def test_xlogyx():
def bad(x, y):
return x * log(y / x)
assert_almost_equal(xlogyx(1., 1.), bad(1., 1.))
assert_almost_equal(xlogyx(1., 2.), bad(1., 2.))
assert_almost_equal(xlogyx(1., 3.), bad(1., 3.))
assert_almost_equal(xlogyx(0., 1.), 0.)
def test_wlogyx():
def bad(w, y, x):
return w * log(y / x)
assert_almost_equal(wlogyx(1., 1., 1.), bad(1., 1., 1.))
assert_almost_equal(wlogyx(1., 2., 3.), bad(1., 2., 3.))
assert_almost_equal(wlogyx(1e-50, 1e-20, 1.), bad(1e-50, 1e-20, 1.))
def test_construct_arg():
arg = (1, 2, 3, 4, 5, 6)
pos = np.array([0, 2, 4], dtype=np.int)
carg = construct_arg(arg, pos)
expected = (1, 3, 5)
# print carg
for i in range(len(carg)):
assert_almost_equal(carg[i], expected[i])
def test_merge_func_code():
funccode, [pf, pg, ph] = merge_func_code(f, g, h)
assert_equal(funccode.co_varnames, ('x', 'y', 'z', 'a', 'b', 'c', 'd'))
exp_pf = [0, 1, 2]
for i in range(len(pf)): assert_almost_equal(pf[i], exp_pf[i])
exp_pg = [0, 3, 4]
for i in range(len(pg)): assert_almost_equal(pg[i], exp_pg[i])
exp_ph = [0, 5, 6]
for i in range(len(ph)): assert_almost_equal(ph[i], exp_ph[i])
def test_merge_func_code_prefix():
funccode, [pf, pg, ph] = merge_func_code(
f, g, h,
prefix=['f_', 'g_', 'h_'],
skip_first=True)
assert_equal(funccode.co_varnames, ('x', 'f_y', 'f_z',
'g_a', 'g_b', 'h_c', 'h_d'))
exp_pf = [0, 1, 2]
for i in range(len(pf)): assert_almost_equal(pf[i], exp_pf[i])
exp_pg = [0, 3, 4]
for i in range(len(pg)): assert_almost_equal(pg[i], exp_pg[i])
exp_ph = [0, 5, 6]
for i in range(len(ph)): assert_almost_equal(ph[i], exp_ph[i])
def test_merge_func_code_factor_list():
funccode, [pf, pg, pk_1, pk_2] = merge_func_code(
f, g,
prefix=['f_', 'g_'],
skip_first=True,
factor_list=[k_1, k_2])
assert_equal(funccode.co_varnames, ('x', 'f_y', 'f_z',
'g_a', 'g_b', 'g_i', 'g_j'))
exp_pf = [0, 1, 2]
for i in range(len(pf)): assert_almost_equal(pf[i], exp_pf[i])
exp_pg = [0, 3, 4]
for i in range(len(pg)): assert_almost_equal(pg[i], exp_pg[i])
exp_pk_1 = [1, 2]
for i in range(len(pk_1)): assert_almost_equal(pk_1[i], exp_pk_1[i])
exp_pk_2 = [5, 6]
for i in range(len(pk_1)): assert_almost_equal(pk_2[i], exp_pk_2[i])
def test_merge_func_code_skip_prefix():
funccode, pos = merge_func_code(
f, f2,
prefix=['f_', 'g_'],
skip_first=True,
skip_prefix=['z'])
assert_equal(funccode.co_varnames, ('x', 'f_y', 'z', 'g_a'))
def test_fast_tuple_equal():
a = (1., 2., 3.)
b = (1., 2., 3.)
ret = fast_tuple_equal(a, b, 0)
assert(ret)
a = (1., 4., 3.)
b = (1., 2., 3.)
ret = fast_tuple_equal(a, b, 0)
assert(not ret)
a = (4., 3.)
b = (1., 4., 3.)
ret = fast_tuple_equal(a, b, 1)
assert(ret)
a = (4., 5.)
b = (1., 4., 3.)
ret = fast_tuple_equal(a, b, 1)
assert(not ret)
a = tuple([])
b = tuple([])
ret = fast_tuple_equal(a, b, 0)
assert(ret)