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Vectorized gamma_method (#207)

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* feat: vectorized gamma_method function added.

* feat: vectorized gamma method generalized to also work on other objects
like Corr or Fit_result.

* feat: alias gamma_method for vectorized gamma_method added.

* docs: example 5 updated to include vectorized gamma_method.

* docs: output of example 5 updated.
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Fabian Joswig 2023-07-19 12:13:20 +01:00 • committed by GitHub
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3 changed files with 76 additions and 46 deletions
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33
tests/obs_test.py
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@ -1,4 +1,5 @@
import autograd.numpy as np
import numpy as np
import autograd.numpy as anp
import os
import copy
import matplotlib.pyplot as plt
@ -126,9 +127,9 @@ def test_function_overloading():
fs = [lambda x: x[0] + x[1], lambda x: x[1] + x[0], lambda x: x[0] - x[1], lambda x: x[1] - x[0],
lambda x: x[0] * x[1], lambda x: x[1] * x[0], lambda x: x[0] / x[1], lambda x: x[1] / x[0],
lambda x: np.exp(x[0]), lambda x: np.sin(x[0]), lambda x: np.cos(x[0]), lambda x: np.tan(x[0]),
lambda x: np.log(x[0]), lambda x: np.sqrt(np.abs(x[0])),
lambda x: np.sinh(x[0]), lambda x: np.cosh(x[0]), lambda x: np.tanh(x[0])]
lambda x: anp.exp(x[0]), lambda x: anp.sin(x[0]), lambda x: anp.cos(x[0]), lambda x: anp.tan(x[0]),
lambda x: anp.log(x[0]), lambda x: anp.sqrt(anp.abs(x[0])),
lambda x: anp.sinh(x[0]), lambda x: anp.cosh(x[0]), lambda x: anp.tanh(x[0])]
for i, f in enumerate(fs):
t1 = f([a, b])
@ -306,9 +307,9 @@ def test_derived_observables():
test_obs = pe.pseudo_Obs(2, 0.1 * (1 + np.random.rand()), 't', int(1000 * (1 + np.random.rand())))
# Check if autograd and numgrad give the same result
d_Obs_ad = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * np.sin(x[0] * x[1]), [test_obs, test_obs])
d_Obs_ad = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * anp.sin(x[0] * x[1]), [test_obs, test_obs])
d_Obs_ad.gamma_method()
d_Obs_fd = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * np.sin(x[0] * x[1]), [test_obs, test_obs], num_grad=True)
d_Obs_fd = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * anp.sin(x[0] * x[1]), [test_obs, test_obs], num_grad=True)
d_Obs_fd.gamma_method()
assert d_Obs_ad == d_Obs_fd
@ -1283,12 +1284,14 @@ def test_format_uncertainty():
pe.obs._format_uncertainty(1, np.NaN)
pe.obs._format_uncertainty(np.NaN, np.inf)
def test_format():
o1 = pe.pseudo_Obs(0.348, 0.0123, "test")
assert o1.__format__("+3") == '+0.3480(123)'
assert o1.__format__("+2") == '+0.348(12)'
assert o1.__format__(" 2") == ' 0.348(12)'
def test_f_string_obs():
o1 = pe.pseudo_Obs(0.348, 0.0123, "test")
print(f"{o1}")
@ -1297,6 +1300,7 @@ def test_f_string_obs():
print(f"{o1:-1}")
print(f"{o1: 8}")
def test_f_string_cobs():
o_real = pe.pseudo_Obs(0.348, 0.0123, "test")
o_imag = pe.pseudo_Obs(0.348, 0.0123, "test")
@ -1307,7 +1311,24 @@ def test_f_string_cobs():
print(f"{o1:-1}")
print(f"{o1: 8}")
def test_compute_drho_fails():
obs = pe.input.json.load_json("tests/data/compute_drho_fails.json.gz")
obs.gm()
assert np.isclose(obs.dvalue, 0.0022150779611891094)
def test_vec_gm():
obs = pe.misc.gen_correlated_data(np.arange(3), np.array([[0.0364 , 0.03627262, 0.03615699],
[0.03627262, 0.03688438, 0.03674798],
[0.03615699, 0.03674798, 0.03732882]]), "qq", 3.8, 1000)
pe.gm(obs[0], S=0)
assert obs[0].S["qq"] == 0
pe.gm(obs, S=1.3)
assert np.all(np.vectorize(lambda x: x.S["qq"])(obs) == 1.3)
aa = np.array([obs, obs, obs])
pe.gamma_method(aa, S=2.2)
assert np.all(np.vectorize(lambda x: x.S["qq"])(aa) == 2.20)
cc = pe.Corr(obs)
pe.gm(cc, S=4.12)
assert np.all(np.vectorize(lambda x: x.S["qq"])(cc.content) == 4.12)