pyerrors/tests/pyerrors_test.py
2021-10-28 18:07:09 +02:00

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Python

import autograd.numpy as np
import os
import random
import string
import copy
import pyerrors as pe
import pytest
np.random.seed(0)
def test_dump():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
test_obs = pe.pseudo_Obs(value, dvalue, 't')
test_obs.dump('test_dump')
new_obs = pe.load_object('test_dump.p')
os.remove('test_dump.p')
assert test_obs.deltas['t'].all() == new_obs.deltas['t'].all()
def test_comparison():
value1 = np.random.normal(0, 100)
test_obs1 = pe.pseudo_Obs(value1, 0.1, 't')
value2 = np.random.normal(0, 100)
test_obs2 = pe.pseudo_Obs(value2, 0.1, 't')
assert (value1 > value2) == (test_obs1 > test_obs2)
assert (value1 < value2) == (test_obs1 < test_obs2)
assert (value1 >= value2) == (test_obs1 >= test_obs2)
assert (value1 <= value2) == (test_obs1 <= test_obs2)
assert test_obs1 >= test_obs1
assert test_obs2 <= test_obs2
assert test_obs1 == test_obs1
assert test_obs2 == test_obs2
assert test_obs1 - test_obs1 == 0.0
assert test_obs1 / test_obs1 == 1.0
assert test_obs1 != value1
assert test_obs2 != value2
assert test_obs1 != test_obs2
assert test_obs2 != test_obs1
def test_function_overloading():
a = pe.pseudo_Obs(17, 2.9, 'e1')
b = pe.pseudo_Obs(4, 0.8, 'e1')
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])]
for i, f in enumerate(fs):
t1 = f([a, b])
t2 = pe.derived_observable(f, [a, b])
c = t2 - t1
assert c.is_zero()
assert np.log(np.exp(b)) == b
assert np.exp(np.log(b)) == b
assert np.sqrt(b ** 2) == b
assert np.sqrt(b) ** 2 == b
def test_overloading_vectorization():
a = np.random.randint(1, 100, 10)
b = pe.pseudo_Obs(4, 0.8, 't')
assert [o.value for o in a * b] == [o.value for o in b * a]
assert [o.value for o in a + b] == [o.value for o in b + a]
assert [o.value for o in a - b] == [-1 * o.value for o in b - a]
assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]]
assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
a = np.random.normal(0.0, 1e10, 10)
b = pe.pseudo_Obs(4, 0.8, 't')
assert [o.value for o in a * b] == [o.value for o in b * a]
assert [o.value for o in a + b] == [o.value for o in b + a]
assert [o.value for o in a - b] == [-1 * o.value for o in b - a]
assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]]
assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
def test_gamma_method():
for data in [np.tile([1, -1], 1000),
np.random.rand(100001),
np.zeros(1195),
np.sin(np.sqrt(2) * np.pi * np.arange(1812))]:
test_obs = pe.Obs([data], ['t'])
test_obs.gamma_method()
assert test_obs.dvalue - test_obs.ddvalue <= np.std(data, ddof=1) / np.sqrt(len(data))
assert test_obs.e_tauint['t'] - 0.5 <= test_obs.e_dtauint['t']
test_obs.gamma_method(tau_exp=10)
assert test_obs.e_tauint['t'] - 10.5 <= test_obs.e_dtauint['t']
def test_gamma_method_persistance():
my_obs = pe.Obs([np.random.rand(730)], ['t'])
my_obs.gamma_method()
value = my_obs.value
dvalue = my_obs.dvalue
ddvalue = my_obs.ddvalue
my_obs = 1.0 * my_obs
my_obs.gamma_method()
assert value == my_obs.value
assert dvalue == my_obs.dvalue
assert ddvalue == my_obs.ddvalue
my_obs.gamma_method()
assert value == my_obs.value
assert dvalue == my_obs.dvalue
assert ddvalue == my_obs.ddvalue
my_obs.gamma_method(S=3.7)
my_obs.gamma_method()
assert value == my_obs.value
assert dvalue == my_obs.dvalue
assert ddvalue == my_obs.ddvalue
def test_covariance_is_variance():
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
test_obs = pe.pseudo_Obs(value, dvalue, 't')
test_obs.gamma_method()
assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
test_obs.gamma_method(e_tag=0)
assert np.abs(test_obs.dvalue ** 2 - pe.covariance(test_obs, test_obs)) <= 10 * np.finfo(np.float64).eps
def test_fft():
value = np.random.normal(5, 100)
dvalue = np.abs(np.random.normal(0, 5))
test_obs1 = pe.pseudo_Obs(value, dvalue, 't', int(500 + 1000 * np.random.rand()))
test_obs2 = copy.deepcopy(test_obs1)
test_obs1.gamma_method()
test_obs2.gamma_method(fft=False)
assert max(np.abs(test_obs1.e_rho[''] - test_obs2.e_rho[''])) <= 10 * np.finfo(np.float64).eps
assert np.abs(test_obs1.dvalue - test_obs2.dvalue) <= 10 * max(test_obs1.dvalue, test_obs2.dvalue) * np.finfo(np.float64).eps
def test_covariance_symmetry():
value1 = np.random.normal(5, 10)
dvalue1 = np.abs(np.random.normal(0, 1))
test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't')
test_obs1.gamma_method()
value2 = np.random.normal(5, 10)
dvalue2 = np.abs(np.random.normal(0, 1))
test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
test_obs2.gamma_method()
cov_ab = pe.covariance(test_obs1, test_obs2)
cov_ba = pe.covariance(test_obs2, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
def test_gamma_method():
# Construct pseudo Obs with random shape
value = np.random.normal(5, 10)
dvalue = np.abs(np.random.normal(0, 1))
test_obs = pe.pseudo_Obs(value, dvalue, 't', int(1000 * (1 + np.random.rand())))
# Test if the error is processed correctly
test_obs.gamma_method(e_tag=1)
assert np.abs(test_obs.value - value) < 1e-12
assert abs(test_obs.dvalue - dvalue) < 1e-10 * dvalue
def test_derived_observables():
# Construct pseudo Obs with random shape
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.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.gamma_method()
assert d_Obs_ad.value == d_Obs_fd.value
assert np.abs(4.0 * np.sin(4.0) - d_Obs_ad.value) < 1000 * np.finfo(np.float64).eps * np.abs(d_Obs_ad.value)
assert np.abs(d_Obs_ad.dvalue-d_Obs_fd.dvalue) < 1000 * np.finfo(np.float64).eps * d_Obs_ad.dvalue
i_am_one = pe.derived_observable(lambda x, **kwargs: x[0] / x[1], [d_Obs_ad, d_Obs_ad])
i_am_one.gamma_method(e_tag=1)
assert i_am_one.value == 1.0
assert i_am_one.dvalue < 2 * np.finfo(np.float64).eps
assert i_am_one.e_dvalue['t'] <= 2 * np.finfo(np.float64).eps
assert i_am_one.e_ddvalue['t'] <= 2 * np.finfo(np.float64).eps
def test_multi_ens_system():
names = []
for i in range(100 + int(np.random.rand() * 50)):
tmp_string = ''
for _ in range(int(2 + np.random.rand() * 4)):
tmp_string += random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits)
names.append(tmp_string)
names = list(set(names))
samples = [np.random.rand(5)] * len(names)
new_obs = pe.Obs(samples, names)
for e_tag_length in range(1, 6):
new_obs.gamma_method(e_tag=e_tag_length)
e_names = sorted(set([n[:e_tag_length] for n in names]))
assert e_names == new_obs.e_names
assert sorted(x for y in sorted(new_obs.e_content.values()) for x in y) == sorted(new_obs.names)
def test_overloaded_functions():
funcs = [np.exp, np.log, np.sin, np.cos, np.tan, np.sinh, np.cosh, np.arcsinh, np.arccosh]
deriv = [np.exp, lambda x: 1 / x, np.cos, lambda x: -np.sin(x), lambda x: 1 / np.cos(x) ** 2, np.cosh, np.sinh, lambda x: 1 / np.sqrt(x ** 2 + 1), lambda x: 1 / np.sqrt(x ** 2 - 1)]
val = 3 + 0.5 * np.random.rand()
dval = 0.3 + 0.4 * np.random.rand()
test_obs = pe.pseudo_Obs(val, dval, 't', int(1000 * (1 + np.random.rand())))
for i, item in enumerate(funcs):
ad_obs = item(test_obs)
fd_obs = pe.derived_observable(lambda x, **kwargs: item(x[0]), [test_obs], num_grad=True)
ad_obs.gamma_method(S=0.01, e_tag=1)
assert np.max((ad_obs.deltas['t'] - fd_obs.deltas['t']) / ad_obs.deltas['t']) < 1e-8, item.__name__
assert np.abs((ad_obs.value - item(val)) / ad_obs.value) < 1e-10, item.__name__
assert np.abs(ad_obs.dvalue - dval * np.abs(deriv[i](val))) < 1e-6, item.__name__
def test_utils():
my_obs = pe.pseudo_Obs(1.0, 0.5, 't')
my_obs.print(0)
my_obs.print(1)
my_obs.print(2)
assert not my_obs.is_zero_within_error()
my_obs.plot_tauint()
my_obs.plot_rho()
my_obs.plot_rep_dist()
my_obs.plot_history()
my_obs.plot_piechart()
assert my_obs > (my_obs - 1)
assert my_obs < (my_obs + 1)
def test_cobs():
obs1 = pe.pseudo_Obs(1.0, 0.1, 't')
obs2 = pe.pseudo_Obs(-0.2, 0.03, 't')
my_cobs = pe.CObs(obs1, obs2)
assert not (my_cobs + my_cobs.conjugate()).real.is_zero()
assert (my_cobs + my_cobs.conjugate()).imag.is_zero()
assert (my_cobs - my_cobs.conjugate()).real.is_zero()
assert not (my_cobs - my_cobs.conjugate()).imag.is_zero()
np.abs(my_cobs)
assert (my_cobs * my_cobs / my_cobs - my_cobs).is_zero()
assert (my_cobs + my_cobs - 2 * my_cobs).is_zero()
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]]]
for other in [3, 1.1, (1.1 - 0.2j), (2.3 + 0j), (0.0 + 7.7j), pe.CObs(obs1), pe.CObs(obs1, obs2)]:
for funcs in fs:
ta = funcs[0]([my_cobs, other])
tb = funcs[1]([my_cobs, other])
diff = ta - tb
assert diff.is_zero()
ta = my_cobs - other
tb = other - my_cobs
diff = ta + tb
assert diff.is_zero()
ta = my_cobs / other
tb = other / my_cobs
diff = ta * tb - 1
assert diff.is_zero()
assert (my_cobs / other * other - my_cobs).is_zero()
assert (other / my_cobs * my_cobs - other).is_zero()
def test_gamma_method_irregular():
N = 20000
arr = np.random.normal(1, .2, size=N)
afull = pe.Obs([arr], ['a'])
configs = np.ones_like(arr)
for i in np.random.uniform(0, len(arr), size=int(.8 * N)):
configs[int(i)] = 0
zero_arr = [arr[i] for i in range(len(arr)) if not configs[i] == 0]
idx = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr], ['a'], idl=[idx])
afull.gamma_method()
a.gamma_method()
expe = (afull.dvalue * np.sqrt(N / np.sum(configs)))
assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
afull.gamma_method(fft=False)
a.gamma_method(fft=False)
expe = (afull.dvalue * np.sqrt(N / np.sum(configs)))
assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
afull.gamma_method(tau_exp=.00001)
a.gamma_method(tau_exp=.00001)
expe = (afull.dvalue * np.sqrt(N / np.sum(configs)))
assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
arr2 = np.random.normal(1, .2, size=N)
afull = pe.Obs([arr, arr2], ['a1', 'a2'])
configs = np.ones_like(arr2)
for i in np.random.uniform(0, len(arr2), size=int(.8*N)):
configs[int(i)] = 0
zero_arr2 = [arr2[i] for i in range(len(arr2)) if not configs[i] == 0]
idx2 = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr, zero_arr2], ['a1', 'a2'], idl=[idx, idx2])
afull.gamma_method(e_tag=1)
a.gamma_method(e_tag=1)
expe = (afull.dvalue * np.sqrt(N / np.sum(configs)))
assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
def gen_autocorrelated_array(inarr, rho):
outarr = np.copy(inarr)
for i in range(1, len(outarr)):
outarr[i] = rho * outarr[i - 1] + np.sqrt(1 - rho**2) * outarr[i]
return outarr
arr = np.random.normal(1, .2, size=N)
carr = gen_autocorrelated_array(arr, .346)
a = pe.Obs([carr], ['a'])
a.gamma_method()
ae = pe.Obs([[carr[i] for i in range(len(carr)) if i % 2 == 0]], ['a'], idl=[[i for i in range(len(carr)) if i % 2 == 0]])
ae.gamma_method()
ao = pe.Obs([[carr[i] for i in range(len(carr)) if i % 2 == 1]], ['a'], idl=[[i for i in range(len(carr)) if i % 2 == 1]])
ao.gamma_method()
assert(ae.e_tauint['a'] < a.e_tauint['a'])
assert((ae.e_tauint['a'] - 4 * ae.e_dtauint['a'] < ao.e_tauint['a']))
assert((ae.e_tauint['a'] + 4 * ae.e_dtauint['a'] > ao.e_tauint['a']))
def test_covariance2_symmetry():
value1 = np.random.normal(5, 10)
dvalue1 = np.abs(np.random.normal(0, 1))
test_obs1 = pe.pseudo_Obs(value1, dvalue1, 't')
test_obs1.gamma_method()
value2 = np.random.normal(5, 10)
dvalue2 = np.abs(np.random.normal(0, 1))
test_obs2 = pe.pseudo_Obs(value2, dvalue2, 't')
test_obs2.gamma_method()
cov_ab = pe.covariance2(test_obs1, test_obs2)
cov_ba = pe.covariance2(test_obs2, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
N = 100
arr = np.random.normal(1, .2, size=N)
configs = np.ones_like(arr)
for i in np.random.uniform(0, len(arr), size=int(.8 * N)):
configs[int(i)] = 0
zero_arr = [arr[i] for i in range(len(arr)) if not configs[i] == 0]
idx = [i + 1 for i in range(len(configs)) if configs[i] == 1]
a = pe.Obs([zero_arr], ['t'], idl=[idx])
a.gamma_method()
assert np.isclose(a.dvalue**2, pe.covariance2(a, a), atol=100, rtol=1e-4)
cov_ab = pe.covariance2(test_obs1, a)
cov_ba = pe.covariance2(a, test_obs1)
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)