mirror of
https://github.com/fjosw/pyerrors.git
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525c61ed20
* tests: failing test for compute_drho edge case added. * tests: example file for failing compute_drho added. * tests: assertion that dvalue stays the same added to compute drho test. * fix: another edge case in computation of drho fixed.
1289 lines
45 KiB
Python
1289 lines
45 KiB
Python
import autograd.numpy as np
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import os
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import copy
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import matplotlib.pyplot as plt
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import pyerrors as pe
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import pytest
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from hypothesis import given, strategies as st
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np.random.seed(0)
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@given(st.lists(st.floats(allow_nan=False, allow_infinity=False, width=32), min_size=5),
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st.text(),
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st.floats(allow_nan=False, allow_infinity=False, width=32, min_value=0))
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def test_fuzzy_obs(data, string, S):
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my_obs = pe.Obs([data], [string])
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my_obs * my_obs
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my_obs.gamma_method(S=S)
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@given(st.floats(allow_nan=False, allow_infinity=False, width=16))
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def test_sin2_cos2(value):
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Obs = pe.pseudo_Obs(value, value * 0.123, "C0")
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iamzero = np.sin(Obs) ** 2 + np.cos(Obs) ** 2 - 1
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assert iamzero.is_zero(atol=1e-6)
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def test_Obs_exceptions():
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with pytest.raises(ValueError):
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pe.Obs([np.random.rand(10)], ['1', '2'])
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with pytest.raises(ValueError):
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pe.Obs([np.random.rand(10)], ['1'], idl=[])
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with pytest.raises(ValueError):
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pe.Obs([np.random.rand(10), np.random.rand(10)], ['1', '1'])
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with pytest.raises(TypeError):
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pe.Obs([np.random.rand(10), np.random.rand(10)], ['1', 1])
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with pytest.raises(TypeError):
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pe.Obs([np.random.rand(10)], [1])
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with pytest.raises(ValueError):
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pe.Obs([np.random.rand(4)], ['name'])
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with pytest.raises(ValueError):
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pe.Obs([np.random.rand(5)], ['1'], idl=[[5, 3, 2 ,4 ,1]])
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with pytest.raises(TypeError):
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pe.Obs([np.random.rand(5)], ['1'], idl=['t'])
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with pytest.raises(ValueError):
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pe.Obs([np.random.rand(5)], ['1'], idl=[range(1, 8)])
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my_obs = pe.Obs([np.random.rand(6)], ['name'])
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my_obs._value = 0.0
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my_obs.details()
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with pytest.raises(Exception):
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my_obs.plot_tauint()
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with pytest.raises(Exception):
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my_obs.plot_rho()
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with pytest.raises(Exception):
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my_obs.plot_rep_dist()
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with pytest.raises(Exception):
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my_obs.plot_piechart()
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with pytest.raises(Exception):
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my_obs.gamma_method(S='2.3')
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with pytest.raises(Exception):
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my_obs.gamma_method(tau_exp=2.3)
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my_obs.gamma_method()
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my_obs.details()
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my_obs.plot_rep_dist()
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my_obs += pe.Obs([np.random.rand(6)], ['name2|r1'], idl=[[1, 3, 4, 5, 6, 7]])
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my_obs += pe.Obs([np.random.rand(6)], ['name2|r2'])
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my_obs.gamma_method()
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my_obs.details()
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obs = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t'])
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one = obs / obs
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one.gamma_method()
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with pytest.raises(Exception):
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one.plot_piechart()
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plt.close('all')
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def test_dump_pickle():
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value = np.random.normal(5, 10)
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dvalue = np.abs(np.random.normal(0, 1))
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test_obs = pe.pseudo_Obs(value, dvalue, 't')
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test_obs.dump('test_dump', datatype="pickle", path=".")
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test_obs.dump('test_dump', datatype="pickle")
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new_obs = pe.load_object('test_dump.p')
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os.remove('test_dump.p')
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assert test_obs == new_obs
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def test_dump_json():
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value = np.random.normal(5, 10)
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dvalue = np.abs(np.random.normal(0, 1))
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test_obs = pe.pseudo_Obs(value, dvalue, 't')
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test_obs.dump('test_dump', dataype="json.gz", path=".")
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test_obs.dump('test_dump', dataype="json.gz")
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new_obs = pe.input.json.load_json("test_dump")
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os.remove('test_dump.json.gz')
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assert test_obs == new_obs
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def test_comparison():
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value1 = np.random.normal(0, 100)
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test_obs1 = pe.pseudo_Obs(value1, 0.1, 't')
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value2 = np.random.normal(0, 100)
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test_obs2 = pe.pseudo_Obs(value2, 0.1, 't')
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assert (value1 > value2) == (test_obs1 > test_obs2)
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assert (value1 < value2) == (test_obs1 < test_obs2)
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assert (value1 >= value2) == (test_obs1 >= test_obs2)
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assert (value1 <= value2) == (test_obs1 <= test_obs2)
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assert test_obs1 >= test_obs1
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assert test_obs2 <= test_obs2
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assert test_obs1 == test_obs1
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assert test_obs2 == test_obs2
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assert test_obs1 - test_obs1 == 0.0
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assert test_obs1 / test_obs1 == 1.0
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assert test_obs1 != value1
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assert test_obs2 != value2
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assert test_obs1 != test_obs2
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assert test_obs2 != test_obs1
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assert +test_obs1 == test_obs1
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assert -test_obs1 == 0 - test_obs1
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def test_function_overloading():
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a = pe.pseudo_Obs(17, 2.9, 'e1')
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b = pe.pseudo_Obs(4, 0.8, 'e1')
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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],
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lambda x: x[0] * x[1], lambda x: x[1] * x[0], lambda x: x[0] / x[1], lambda x: x[1] / x[0],
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lambda x: np.exp(x[0]), lambda x: np.sin(x[0]), lambda x: np.cos(x[0]), lambda x: np.tan(x[0]),
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lambda x: np.log(x[0]), lambda x: np.sqrt(np.abs(x[0])),
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lambda x: np.sinh(x[0]), lambda x: np.cosh(x[0]), lambda x: np.tanh(x[0])]
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for i, f in enumerate(fs):
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t1 = f([a, b])
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t2 = pe.derived_observable(f, [a, b])
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c = t2 - t1
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assert c.is_zero()
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assert np.log(np.exp(b)) == b
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assert np.exp(np.log(b)) == b
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assert np.sqrt(b ** 2) == b
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assert np.sqrt(b) ** 2 == b
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np.arcsin(1 / b)
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np.arccos(1 / b)
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np.arctan(1 / b)
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np.arctanh(1 / b)
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np.sinc(1 / b)
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b ** b
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0.5 ** b
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b ** 0.5
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def test_overloading_vectorization():
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a = np.random.randint(1, 100, 10)
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b = pe.pseudo_Obs(4, 0.8, 't')
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assert [o.value for o in a * b] == [o.value for o in b * a]
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assert [o.value for o in a + b] == [o.value for o in b + a]
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assert [o.value for o in a - b] == [-1 * o.value for o in b - a]
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assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]]
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assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
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a = np.random.normal(0.0, 1e10, 10)
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b = pe.pseudo_Obs(4, 0.8, 't')
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assert [o.value for o in a * b] == [o.value for o in b * a]
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assert [o.value for o in a + b] == [o.value for o in b + a]
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assert [o.value for o in a - b] == [-1 * o.value for o in b - a]
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assert [o.value for o in a / b] == [o.value for o in [p / b for p in a]]
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assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
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def test_gamma_method_standard_data():
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for data in [np.tile([1, -1], 1000),
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np.zeros(1195),
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np.sin(np.sqrt(2) * np.pi * np.arange(1812))]:
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test_obs = pe.Obs([data], ['t'])
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test_obs.gamma_method()
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assert test_obs.dvalue - test_obs.ddvalue <= np.std(data, ddof=1) / np.sqrt(len(data))
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assert test_obs.e_tauint['t'] - 0.5 <= test_obs.e_dtauint['t']
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test_obs.gamma_method(tau_exp=10)
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assert test_obs.e_tauint['t'] - 10.5 <= test_obs.e_dtauint['t']
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def test_gamma_method_no_windowing():
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for iteration in range(50):
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obs = pe.Obs([np.random.normal(1.02, 0.02, 733 + np.random.randint(1000))], ['ens'])
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obs.gamma_method(S=0)
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assert obs.e_tauint['ens'] == 0.5
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assert np.isclose(np.sqrt(np.var(obs.deltas['ens'], ddof=1) / obs.shape['ens']), obs.dvalue)
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obs.gamma_method(S=1.1)
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assert obs.e_tauint['ens'] > 0.5
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with pytest.raises(Exception):
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obs.gamma_method(S=-0.2)
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def test_gamma_method_persistance():
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my_obs = pe.Obs([np.random.rand(730)], ['t'])
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my_obs.gamma_method()
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value = my_obs.value
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dvalue = my_obs.dvalue
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ddvalue = my_obs.ddvalue
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my_obs = 1.0 * my_obs
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my_obs.gamma_method()
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assert value == my_obs.value
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assert dvalue == my_obs.dvalue
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assert ddvalue == my_obs.ddvalue
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my_obs.gamma_method()
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assert value == my_obs.value
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assert dvalue == my_obs.dvalue
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assert ddvalue == my_obs.ddvalue
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my_obs.gamma_method(S=3.7)
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my_obs.gamma_method()
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assert value == my_obs.value
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assert dvalue == my_obs.dvalue
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assert ddvalue == my_obs.ddvalue
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def test_gamma_method_kwargs():
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my_obs = pe.Obs([np.random.normal(1, 0.8, 5)], ['ens'], idl=[[1, 2, 3, 6, 17]])
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pe.Obs.S_dict['ens13.7'] = 3
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my_obs.gamma_method()
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assert my_obs.S['ens'] == pe.Obs.S_global
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assert my_obs.tau_exp['ens'] == pe.Obs.tau_exp_global
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assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_global
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my_obs.gamma_method(S=3.71)
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assert my_obs.S['ens'] == 3.71
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assert my_obs.tau_exp['ens'] == pe.Obs.tau_exp_global
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assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_global
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my_obs.gamma_method(tau_exp=17)
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assert my_obs.S['ens'] == pe.Obs.S_global
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assert my_obs.tau_exp['ens'] == 17
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assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_global
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my_obs.gamma_method(tau_exp=1.7, N_sigma=2.123)
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assert my_obs.S['ens'] == pe.Obs.S_global
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assert my_obs.tau_exp['ens'] == 1.7
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assert my_obs.N_sigma['ens'] == 2.123
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pe.Obs.S_dict['ens'] = 3
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pe.Obs.S_dict['ens|23'] = 7
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my_obs.gamma_method()
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assert my_obs.S['ens'] == pe.Obs.S_dict['ens'] == 3
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assert my_obs.tau_exp['ens'] == pe.Obs.tau_exp_global
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assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_global
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pe.Obs.tau_exp_dict['ens'] = 4
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pe.Obs.N_sigma_dict['ens'] = 4
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my_obs.gamma_method()
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assert my_obs.S['ens'] == pe.Obs.S_dict['ens'] == 3
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assert my_obs.tau_exp['ens'] == pe.Obs.tau_exp_dict['ens'] == 4
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assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_dict['ens'] == 4
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my_obs.gamma_method(S=1.1, tau_exp=1.2, N_sigma=1.3)
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assert my_obs.S['ens'] == 1.1
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assert my_obs.tau_exp['ens'] == 1.2
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assert my_obs.N_sigma['ens'] == 1.3
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pe.Obs.S_dict = {}
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pe.Obs.tau_exp_dict = {}
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pe.Obs.N_sigma_dict = {}
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my_obs = pe.Obs([np.random.normal(1, 0.8, 5)], ['ens'])
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my_obs.gamma_method()
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assert my_obs.S['ens'] == pe.Obs.S_global
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assert my_obs.tau_exp['ens'] == pe.Obs.tau_exp_global
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assert my_obs.N_sigma['ens'] == pe.Obs.N_sigma_global
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def test_fft():
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value = np.random.normal(5, 100)
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dvalue = np.abs(np.random.normal(0, 5))
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test_obs1 = pe.pseudo_Obs(value, dvalue, 't', int(500 + 1000 * np.random.rand()))
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test_obs2 = copy.deepcopy(test_obs1)
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test_obs1.gamma_method()
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test_obs2.gamma_method(fft=False)
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assert max(np.abs(test_obs1.e_rho['t'] - test_obs2.e_rho['t'])) <= 10 * np.finfo(np.float64).eps
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assert np.abs(test_obs1.dvalue - test_obs2.dvalue) <= 10 * max(test_obs1.dvalue, test_obs2.dvalue) * np.finfo(np.float64).eps
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def test_gamma_method_uncorrelated():
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# Construct pseudo Obs with random shape
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value = np.random.normal(5, 10)
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dvalue = np.abs(np.random.normal(0, 1))
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test_obs = pe.pseudo_Obs(value, dvalue, 't', int(1000 * (1 + np.random.rand())))
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# Test if the error is processed correctly
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test_obs.gamma_method()
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assert np.abs(test_obs.value - value) < 1e-12
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assert abs(test_obs.dvalue - dvalue) < 1e-10 * dvalue
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def test_derived_observables():
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# Construct pseudo Obs with random shape
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test_obs = pe.pseudo_Obs(2, 0.1 * (1 + np.random.rand()), 't', int(1000 * (1 + np.random.rand())))
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# Check if autograd and numgrad give the same result
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d_Obs_ad = pe.derived_observable(lambda x, **kwargs: x[0] * x[1] * np.sin(x[0] * x[1]), [test_obs, test_obs])
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d_Obs_ad.gamma_method()
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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)
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d_Obs_fd.gamma_method()
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assert d_Obs_ad == d_Obs_fd
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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)
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assert np.abs(d_Obs_ad.dvalue-d_Obs_fd.dvalue) < 1000 * np.finfo(np.float64).eps * d_Obs_ad.dvalue
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i_am_one = pe.derived_observable(lambda x, **kwargs: x[0] / x[1], [d_Obs_ad, d_Obs_ad])
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i_am_one.gamma_method()
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assert i_am_one == 1.0
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assert i_am_one.dvalue < 2 * np.finfo(np.float64).eps
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assert i_am_one.e_dvalue['t'] <= 2 * np.finfo(np.float64).eps
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assert i_am_one.e_ddvalue['t'] <= 2 * np.finfo(np.float64).eps
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def test_multi_ens():
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names = ['A0', 'A1|r001', 'A1|r002']
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test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
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assert test_obs.e_names == ['A0', 'A1']
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assert test_obs.e_content['A0'] == ['A0']
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assert test_obs.e_content['A1'] == ['A1|r001', 'A1|r002']
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my_sum = 0
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ensembles = []
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for i in range(100):
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my_sum += pe.Obs([np.random.rand(50)], [str(i)])
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ensembles.append(str(i))
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assert my_sum.e_names == sorted(ensembles)
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def test_multi_ens2():
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names = ['ens', 'e', 'en', 'e|r010', 'E|er', 'ens|', 'Ens|34', 'ens|r548984654ez4e3t34terh']
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my_sum = 0
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for name in names:
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my_sum += pe.pseudo_Obs(1, 0.1, name)
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assert my_sum.e_names == ['E', 'Ens', 'e', 'en', 'ens']
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assert my_sum.e_content == {'E': ['E|er'],
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'Ens': ['Ens|34'],
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'e': ['e|r010', 'e'],
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'en': ['en'],
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'ens': ['ens|', 'ens|r548984654ez4e3t34terh', 'ens']}
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def test_overloaded_functions():
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funcs = [np.exp, np.log, np.sin, np.cos, np.tan, np.sinh, np.cosh, np.arcsinh, np.arccosh]
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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)]
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val = 3 + 0.5 * np.random.rand()
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dval = 0.3 + 0.4 * np.random.rand()
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test_obs = pe.pseudo_Obs(val, dval, 't', int(1000 * (1 + np.random.rand())))
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for i, item in enumerate(funcs):
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ad_obs = item(test_obs)
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fd_obs = pe.derived_observable(lambda x, **kwargs: item(x[0]), [test_obs], num_grad=True)
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ad_obs.gamma_method(S=0.01)
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assert np.max((ad_obs.deltas['t'] - fd_obs.deltas['t']) / ad_obs.deltas['t']) < 1e-8, item.__name__
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assert np.abs((ad_obs.value - item(val)) / ad_obs.value) < 1e-10, item.__name__
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assert np.abs(ad_obs.dvalue - dval * np.abs(deriv[i](val))) < 1e-6, item.__name__
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def test_utils():
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zero_pseudo_obs = pe.pseudo_Obs(1.0, 0.0, 'null')
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my_obs = pe.pseudo_Obs(1.0, 0.5, 't|r01')
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my_obs += pe.pseudo_Obs(1.0, 0.5, 't|r02')
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str(my_obs)
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for tau_exp in [0, 5]:
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my_obs.gamma_method(tau_exp=tau_exp)
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my_obs.tag = "Test description"
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my_obs.details(False)
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my_obs.details(True)
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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(True)
|
|
my_obs.plot_history(False)
|
|
my_obs.plot_piechart()
|
|
assert my_obs > (my_obs - 1)
|
|
assert my_obs < (my_obs + 1)
|
|
float(my_obs)
|
|
str(my_obs)
|
|
plt.close('all')
|
|
|
|
|
|
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 +my_cobs == my_cobs
|
|
assert -my_cobs == 0 - my_cobs
|
|
my_cobs == my_cobs
|
|
str(my_cobs)
|
|
repr(my_cobs)
|
|
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_cobs_overloading():
|
|
obs = pe.pseudo_Obs(1.1, 0.1, 't')
|
|
cobs = pe.CObs(obs, obs)
|
|
|
|
cobs + obs
|
|
obs + cobs
|
|
|
|
cobs - obs
|
|
obs - cobs
|
|
|
|
cobs * obs
|
|
obs * cobs
|
|
|
|
cobs / obs
|
|
obs / cobs
|
|
|
|
|
|
def test_reweighting():
|
|
my_obs = pe.Obs([np.random.rand(1000)], ['t'])
|
|
assert not my_obs.reweighted
|
|
r_obs = pe.reweight(my_obs, [my_obs])
|
|
assert r_obs[0].reweighted
|
|
r_obs2 = r_obs[0] * my_obs
|
|
assert r_obs2.reweighted
|
|
|
|
my_irregular_obs = pe.Obs([np.random.rand(500)], ['t'], idl=[range(1, 1001, 2)])
|
|
assert not my_irregular_obs.reweighted
|
|
r_obs = pe.reweight(my_obs, [my_irregular_obs], all_configs=True)
|
|
r_obs = pe.reweight(my_obs, [my_irregular_obs], all_configs=False)
|
|
r_obs = pe.reweight(my_obs, [my_obs])
|
|
assert r_obs[0].reweighted
|
|
r_obs2 = r_obs[0] * my_obs
|
|
assert r_obs2.reweighted
|
|
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
|
|
with pytest.raises(Exception):
|
|
pe.reweight(my_obs, [my_covobs])
|
|
my_obs2 = pe.Obs([np.random.rand(1000)], ['t2'])
|
|
with pytest.raises(Exception):
|
|
pe.reweight(my_obs, [my_obs + my_obs2])
|
|
with pytest.raises(Exception):
|
|
pe.reweight(my_irregular_obs, [my_obs])
|
|
|
|
|
|
def test_merge_obs():
|
|
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
|
|
my_obs2 = pe.Obs([np.random.rand(100)], ['q'], idl=[range(1, 200, 2)])
|
|
merged = pe.merge_obs([my_obs1, my_obs2])
|
|
diff = merged - my_obs2 - my_obs1
|
|
assert diff == -(my_obs1.value + my_obs2.value) / 2
|
|
with pytest.raises(Exception):
|
|
pe.merge_obs([my_obs1, my_obs1])
|
|
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
|
|
with pytest.raises(Exception):
|
|
pe.merge_obs([my_obs1, my_covobs])
|
|
|
|
|
|
|
|
def test_merge_obs_r_values():
|
|
a1 = pe.pseudo_Obs(1.1, .1, 'a|1')
|
|
a2 = pe.pseudo_Obs(1.2, .1, 'a|2')
|
|
a = pe.merge_obs([a1, a2])
|
|
|
|
assert np.isclose(a.r_values['a|1'], a1.value)
|
|
assert np.isclose(a.r_values['a|2'], a2.value)
|
|
assert np.isclose(a.value, np.mean([a1.value, a2.value]))
|
|
|
|
|
|
def test_correlate():
|
|
my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
|
|
my_obs2 = pe.Obs([np.random.rand(100)], ['t'])
|
|
corr1 = pe.correlate(my_obs1, my_obs2)
|
|
corr2 = pe.correlate(my_obs2, my_obs1)
|
|
assert corr1 == corr2
|
|
|
|
my_obs3 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(2, 102)])
|
|
with pytest.raises(Exception):
|
|
pe.correlate(my_obs1, my_obs3)
|
|
|
|
my_obs4 = pe.Obs([np.random.rand(99)], ['t'])
|
|
with pytest.raises(Exception):
|
|
pe.correlate(my_obs1, my_obs4)
|
|
|
|
my_obs5 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
|
|
my_obs6 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
|
|
corr3 = pe.correlate(my_obs5, my_obs6)
|
|
assert my_obs5.idl == corr3.idl
|
|
|
|
my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
|
|
with pytest.raises(Exception):
|
|
pe.correlate(my_obs1, my_new_obs)
|
|
my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
|
|
with pytest.raises(Exception):
|
|
pe.correlate(my_covobs, my_covobs)
|
|
r_obs = pe.reweight(my_obs1, [my_obs1])[0]
|
|
with pytest.warns(RuntimeWarning):
|
|
pe.correlate(r_obs, r_obs)
|
|
|
|
|
|
def test_merge_idx():
|
|
assert pe.obs._merge_idx([range(10, 1010, 10), range(10, 1010, 50)]) == range(10, 1010, 10)
|
|
assert isinstance(pe.obs._merge_idx([range(10, 1010, 10), range(10, 1010, 50)]), range)
|
|
assert pe.obs._merge_idx([range(500, 6050, 50), range(500, 6250, 250)]) == range(500, 6001, 50)
|
|
assert isinstance(pe.obs._merge_idx([range(500, 6050, 50), range(500, 6250, 250)]), range)
|
|
assert pe.obs._merge_idx([range(1, 1011, 2), range(1, 1010, 1)]) == range(1, 1010, 1)
|
|
assert isinstance(pe.obs._merge_idx([range(1, 1011, 2), range(1, 1010, 1)]), range)
|
|
assert pe.obs._merge_idx([range(1, 100, 2), range(2, 100, 2)]) == range(1, 100, 1)
|
|
assert isinstance(pe.obs._merge_idx([range(1, 100, 2), range(2, 100, 2)]), range)
|
|
|
|
for j in range(5):
|
|
idll = [range(1, int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
|
|
assert pe.obs._merge_idx(idll) == sorted(set().union(*idll))
|
|
|
|
for j in range(5):
|
|
idll = [range(int(round(np.random.uniform(1, 28))), int(round(np.random.uniform(300, 700))), int(round(np.random.uniform(1, 14)))) for i in range(10)]
|
|
assert pe.obs._merge_idx(idll) == sorted(set().union(*idll))
|
|
|
|
idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)]
|
|
new_idx = pe.obs._merge_idx(idl)
|
|
assert(new_idx[-1] > new_idx[0])
|
|
for i in range(1, len(new_idx)):
|
|
assert(new_idx[i - 1] < new_idx[i])
|
|
|
|
|
|
def test_intersection_idx():
|
|
assert pe.obs._intersection_idx([range(1, 100), range(1, 100), range(1, 100)]) == range(1, 100)
|
|
assert pe.obs._intersection_idx([range(1, 100, 10), range(1, 100, 2)]) == range(1, 100, 10)
|
|
assert pe.obs._intersection_idx([range(10, 1010, 10), range(10, 1010, 50)]) == range(10, 1010, 50)
|
|
assert pe.obs._intersection_idx([range(500, 6050, 50), range(500, 6250, 250)]) == range(500, 6001, 250)
|
|
assert pe.obs._intersection_idx([range(1, 1011, 2), range(1, 1010, 1)]) == range(1, 1010, 2)
|
|
idll = [range(1, 100, 2), range(5, 105, 1)]
|
|
assert pe.obs._intersection_idx(idll) == range(5, 100, 2)
|
|
assert isinstance(pe.obs._intersection_idx(idll), range)
|
|
idll = [range(1, 100, 2), list(range(5, 105, 1))]
|
|
assert pe.obs._intersection_idx(idll) == range(5, 100, 2)
|
|
assert isinstance(pe.obs._intersection_idx(idll), range)
|
|
|
|
for ids in [[list(range(1, 80, 3)), list(range(1, 100, 2))], [range(1, 80, 3), range(1, 100, 2), range(1, 100, 7)]]:
|
|
interlist = pe.obs._intersection_idx([list(o) for o in ids])
|
|
listinter = list(pe.obs._intersection_idx(ids))
|
|
assert len(interlist) == len(listinter)
|
|
assert all([o in listinter for o in interlist])
|
|
assert all([o in interlist for o in listinter])
|
|
|
|
|
|
def test_merge_intersection():
|
|
for idl_list in [[range(1, 100), range(1, 100), range(1, 100)],
|
|
[range(4, 80, 6), range(4, 80, 6)],
|
|
[[0, 2, 8, 19, 205], [0, 2, 8, 19, 205]]]:
|
|
assert pe.obs._merge_idx(idl_list) == pe.obs._intersection_idx(idl_list)
|
|
|
|
|
|
def test_intersection_reduce():
|
|
range1 = range(1, 2000, 2)
|
|
range2 = range(2, 2001, 8)
|
|
|
|
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
|
|
obs_merge = obs1 + pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
|
|
|
|
intersection = pe.obs._intersection_idx([o.idl["ens"] for o in [obs1, obs_merge]])
|
|
coll = pe.obs._reduce_deltas(obs_merge.deltas["ens"], obs_merge.idl["ens"], range1)
|
|
|
|
assert np.allclose(coll, obs1.deltas["ens"] * (len(obs_merge.idl["ens"]) / len(range1)))
|
|
|
|
|
|
def test_irregular_error_propagation():
|
|
obs_list = [pe.Obs([np.random.rand(100)], ['t']),
|
|
pe.Obs([np.random.rand(50)], ['t'], idl=[range(1, 100, 2)]),
|
|
pe.Obs([np.random.rand(50)], ['t'], idl=[np.arange(1, 100, 2)]),
|
|
pe.Obs([np.random.rand(6)], ['t'], idl=[[4, 18, 27, 29, 57, 80]]),
|
|
pe.Obs([np.random.rand(50)], ['t'], idl=[list(range(1, 26)) + list(range(50, 100, 2))])]
|
|
for obs1 in obs_list:
|
|
obs1.details()
|
|
for obs2 in obs_list:
|
|
assert obs1 == (obs1 / obs2) * obs2
|
|
assert obs1 == (obs1 * obs2) / obs2
|
|
assert obs1 == obs1 * (obs2 / obs2)
|
|
assert obs1 == (obs1 + obs2) - obs2
|
|
assert obs1 == obs1 + (obs2 - obs2)
|
|
|
|
|
|
def test_gamma_method_consistent():
|
|
dat = np.sin(np.arange(100) / 100)
|
|
for idl in [np.arange(100), np.arange(0, 1000, 10)]:
|
|
my_obs = pe.Obs([dat], ["test_ens"], idl=[idl])
|
|
assert np.isclose(my_obs.value, 0.4554865083873183)
|
|
|
|
my_obs.gm(S=0)
|
|
assert np.isclose(my_obs.dvalue, 0.02495954189079061)
|
|
my_obs.gm()
|
|
assert np.isclose(my_obs.dvalue, 0.11817931680985193)
|
|
|
|
|
|
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()
|
|
ad = a.dvalue
|
|
|
|
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)
|
|
assert np.abs(a.dvalue - ad) <= 10 * max(a.dvalue, ad) * np.finfo(np.float64).eps
|
|
|
|
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()
|
|
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)
|
|
|
|
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()
|
|
|
|
arrt = [carr[i] for i in range(len(carr)) if i % 2 == 1]
|
|
idlt = [i for i in range(len(carr)) if i % 2 == 1]
|
|
for el in [int(e) for e in N * np.random.uniform(size=10)]:
|
|
arrt = arrt[:el] + arrt[el + 1:]
|
|
idlt = idlt[:el] + idlt[el + 1:]
|
|
ai = pe.Obs([arrt], ['a'], idl=[idlt])
|
|
ai.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']))
|
|
assert((ai.e_tauint['a'] - 4 * ai.e_dtauint['a'] < ao.e_tauint['a']))
|
|
assert((ai.e_tauint['a'] + 4 * ai.e_dtauint['a'] > ao.e_tauint['a']))
|
|
|
|
a = pe.pseudo_Obs(1, .1, 'a', samples=10)
|
|
a.idl['a'] = range(4, 15)
|
|
b = pe.pseudo_Obs(1, .1, 'a', samples=151)
|
|
b.idl['a'] = range(4, 608, 4)
|
|
ol = [a, b]
|
|
o = (ol[0] - ol[1]) / (ol[1])
|
|
|
|
N = 1000
|
|
dat = gen_autocorrelated_array(np.random.normal(1, .2, size=N), .8)
|
|
|
|
idl_a = list(range(0, 1001, 1))
|
|
idl_a.remove(101)
|
|
|
|
oa = pe.Obs([dat], ["ens1"], idl=[idl_a])
|
|
oa.gamma_method()
|
|
tau_a = oa.e_tauint["ens1"]
|
|
|
|
idl_b = list(range(0, 10010, 10))
|
|
idl_b.remove(1010)
|
|
|
|
ob = pe.Obs([dat], ["ens1"], idl=[idl_b])
|
|
ob.gamma_method()
|
|
tau_b = ob.e_tauint["ens1"]
|
|
|
|
assert np.isclose(tau_a, tau_b)
|
|
|
|
dat = [np.random.normal(loc=1., size=10) for i in range(2)]
|
|
idl = [[0, 2, 4, 8, 10, 12, 14, 16, 18, 20], np.arange(0, 20, 2)]
|
|
o = pe.Obs(dat, ['A|r1', 'A|r2'], idl=idl)
|
|
o.gm()
|
|
assert(pe.obs._determine_gap(o, o.e_content, 'A') == 2)
|
|
dat = [np.random.normal(loc=1., size=10) for i in range(3)]
|
|
idl = [[0, 2, 4, 8, 10, 12, 14, 16, 18, 20], np.arange(0, 20, 2), range(10)]
|
|
o = pe.Obs(dat, ['A|r1', 'A|r2', 'A|r5'], idl=idl)
|
|
o.gm()
|
|
assert(pe.obs._determine_gap(o, o.e_content, 'A') == 1)
|
|
|
|
dat = np.sin(np.arange(100) / 100)
|
|
|
|
idl = [np.arange(100), np.arange(0, 1000, 10), list(np.arange(0, 100, 10)) + list(np.arange(180, 1080, 10)), range(1, 500, 5)]
|
|
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
|
|
my_obs.gm()
|
|
idl = idl[1:]
|
|
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
|
|
my_obs.gm()
|
|
idl += [range(1, 400, 4)]
|
|
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
|
|
with pytest.raises(Exception):
|
|
my_obs.gm()
|
|
|
|
# check cases where tau is large compared to the chain length
|
|
N = 15
|
|
for i in range(10):
|
|
arr = np.random.normal(1, .2, size=N)
|
|
for rho in .05 * np.arange(20):
|
|
carr = gen_autocorrelated_array(arr, rho)
|
|
a = pe.Obs([carr], ['a'])
|
|
a.gm()
|
|
|
|
arr = np.random.normal(1, .2, size=999)
|
|
carr = gen_autocorrelated_array(arr, .8)
|
|
o = pe.Obs([carr], ['test'])
|
|
o.gamma_method()
|
|
no = np.NaN * o
|
|
no.gamma_method()
|
|
o.idl['test'] = range(1, 1998, 2)
|
|
o.gamma_method()
|
|
no = np.NaN * o
|
|
no.gamma_method()
|
|
|
|
|
|
def test_irregular_gapped_dtauint():
|
|
my_idl = list(range(0, 5010, 10))
|
|
my_idl.remove(400)
|
|
my_idl2 = list(range(0, 501, 1))
|
|
my_idl2.remove(40)
|
|
|
|
for i in range(42):
|
|
my_data = np.random.normal(1.1, 0.2, 500)
|
|
obs = pe.Obs([my_data], ["B1"], idl=[my_idl])
|
|
obs.gamma_method()
|
|
|
|
obs2 = pe.Obs([my_data], ["B2"], idl=[my_idl2])
|
|
obs2.gamma_method()
|
|
|
|
assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
|
|
assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
|
|
assert np.isclose(obs.e_dvalue["B1"], obs2.e_dvalue["B2"])
|
|
assert np.isclose(obs.e_ddvalue["B1"], obs2.e_ddvalue["B2"])
|
|
assert len(obs.e_rho["B1"]) == len(obs2.e_rho["B2"])
|
|
|
|
obs.gamma_method(tau_exp=1)
|
|
obs2.gamma_method(tau_exp=1)
|
|
|
|
assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
|
|
assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
|
|
assert np.isclose(obs.e_dvalue["B1"], obs2.e_dvalue["B2"])
|
|
assert np.isclose(obs.e_ddvalue["B1"], obs2.e_ddvalue["B2"])
|
|
assert len(obs.e_rho["B1"]) == len(obs2.e_rho["B2"])
|
|
|
|
obs.gamma_method(S=0)
|
|
obs2.gamma_method(S=0)
|
|
|
|
assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
|
|
assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
|
|
assert np.isclose(obs.e_dvalue["B1"], obs2.e_dvalue["B2"])
|
|
assert np.isclose(obs.e_ddvalue["B1"], obs2.e_ddvalue["B2"])
|
|
|
|
|
|
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.isclose(test_obs.dvalue ** 2, pe.covariance([test_obs, test_obs])[0, 1])
|
|
test_obs = test_obs + pe.pseudo_Obs(value, dvalue, 'q', 200)
|
|
test_obs.gamma_method()
|
|
assert np.isclose(test_obs.dvalue ** 2, pe.covariance([test_obs, test_obs])[0, 1])
|
|
|
|
|
|
def test_covariance_vs_numpy():
|
|
N = 1078
|
|
data1 = np.random.normal(2.5, 0.2, N)
|
|
data2 = np.random.normal(0.5, 0.08, N)
|
|
data3 = np.random.normal(-178, 5, N)
|
|
uncorr = np.row_stack([data1, data2, data3])
|
|
corr = np.random.multivariate_normal([0.0, 17, -0.0487], [[1.0, 0.6, -0.22], [0.6, 0.8, 0.01], [-0.22, 0.01, 1.9]], N).T
|
|
|
|
for X in [uncorr, corr]:
|
|
obs1 = pe.Obs([X[0]], ["ens1"])
|
|
obs2 = pe.Obs([X[1]], ["ens1"])
|
|
obs3 = pe.Obs([X[2]], ["ens1"])
|
|
obs1.gamma_method(S=0.0)
|
|
obs2.gamma_method(S=0.0)
|
|
obs3.gamma_method(S=0.0)
|
|
pe_cov = pe.covariance([obs1, obs2, obs3])
|
|
np_cov = np.cov(X) / N
|
|
assert np.allclose(pe_cov, np_cov, atol=1e-14)
|
|
|
|
|
|
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])[0, 1]
|
|
cov_ba = pe.covariance([test_obs2, test_obs1])[0, 1]
|
|
assert np.isclose(cov_ab, cov_ba)
|
|
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.covariance([a, a])[0, 1], atol=100, rtol=1e-4)
|
|
|
|
cov_ab = pe.covariance([test_obs1, a])[0, 1]
|
|
cov_ba = pe.covariance([a, test_obs1])[0, 1]
|
|
assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
|
|
assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps)
|
|
|
|
|
|
def test_covariance_sum():
|
|
length = 2
|
|
t_fac = 0.4
|
|
tt = pe.misc.gen_correlated_data(np.zeros(length), 0.99 * np.ones((length, length)) + 0.01 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=1000)
|
|
[o.gamma_method(S=0) for o in tt]
|
|
|
|
t_cov = pe.covariance(tt)
|
|
|
|
my_sum = tt[0] + tt[1]
|
|
my_sum.gamma_method(S=0)
|
|
e_cov = (my_sum.dvalue ** 2 - tt[0].dvalue ** 2 - tt[1].dvalue ** 2) / 2
|
|
|
|
assert np.isclose(e_cov, t_cov[0, 1])
|
|
|
|
|
|
def test_covariance_positive_semidefinite():
|
|
length = 64
|
|
t_fac = 1.5
|
|
tt = pe.misc.gen_correlated_data(np.zeros(length), 0.99999 * np.ones((length, length)) + 0.00001 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=1000)
|
|
[o.gamma_method() for o in tt]
|
|
cov = pe.covariance(tt)
|
|
assert np.all(np.linalg.eigh(cov)[0] >= -1e-15)
|
|
|
|
|
|
def test_covariance_factorizing():
|
|
length = 2
|
|
t_fac = 1.5
|
|
|
|
tt = pe.misc.gen_correlated_data(np.zeros(length), 0.75 * np.ones((length, length)) + 0.8 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=1000)
|
|
[o.gamma_method() for o in tt]
|
|
|
|
mt0 = -tt[0]
|
|
mt0.gamma_method()
|
|
|
|
assert np.isclose(pe.covariance([mt0, tt[1]])[0, 1], -pe.covariance(tt)[0, 1])
|
|
|
|
|
|
def test_covariance_smooth_eigenvalues():
|
|
for c_coeff in range(0, 14, 2):
|
|
length = 14
|
|
sm = 5
|
|
t_fac = 1.5
|
|
tt = pe.misc.gen_correlated_data(np.zeros(length), 1 - 0.1 ** c_coeff * np.ones((length, length)) + 0.1 ** c_coeff * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=200)
|
|
[o.gamma_method() for o in tt]
|
|
full_corr = pe.covariance(tt, correlation=True)
|
|
cov = pe.covariance(tt, smooth=sm, correlation=True)
|
|
|
|
full_evals = np.linalg.eigh(full_corr)[0]
|
|
sm_length = np.where(full_evals < np.mean(full_evals[:-sm]))[0][-1]
|
|
|
|
evals = np.linalg.eigh(cov)[0]
|
|
assert np.all(np.isclose(evals[:sm_length], evals[0], atol=1e-8))
|
|
|
|
|
|
def test_covariance_alternation():
|
|
length = 12
|
|
t_fac = 2.5
|
|
|
|
tt1 = pe.misc.gen_correlated_data(np.zeros(length), -0.00001 * np.ones((length, length)) + 0.002 * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=88)
|
|
tt2 = pe.misc.gen_correlated_data(np.zeros(length), 0.9999 * np.ones((length, length)) + 0.0001 * np.diag(np.ones(length)), 'another_test|r0', tau=0.7 + t_fac * np.random.rand(length), samples=73)
|
|
tt3 = pe.misc.gen_correlated_data(np.zeros(length), 0.9999 * np.ones((length, length)) + 0.0001 * np.diag(np.ones(length)), 'another_test|r1', tau=0.7 + t_fac * np.random.rand(length), samples=91)
|
|
|
|
tt = np.array(tt1) + (np.array(tt2) + np.array(tt3))
|
|
tt *= np.resize([1, -1], length)
|
|
|
|
[o.gamma_method() for o in tt]
|
|
cov = pe.covariance(tt, True)
|
|
|
|
assert np.all(np.linalg.eigh(cov)[0] > -1e-15)
|
|
|
|
|
|
def test_covariance_correlation():
|
|
test_obs = pe.pseudo_Obs(-4, 0.8, 'test', samples=784)
|
|
assert np.allclose(pe.covariance([test_obs, test_obs, test_obs], correlation=True), np.ones((3, 3)))
|
|
|
|
|
|
def test_covariance_rank_deficient():
|
|
obs = []
|
|
for i in range(5):
|
|
obs.append(pe.pseudo_Obs(1.0, 0.1, 'test', 5))
|
|
|
|
with pytest.warns(RuntimeWarning):
|
|
pe.covariance(obs)
|
|
|
|
|
|
def test_covariance_idl():
|
|
range1 = range(10, 1010, 10)
|
|
range2 = range(10, 1010, 50)
|
|
|
|
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
|
|
obs2 = pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
|
|
obs1.gamma_method()
|
|
obs2.gamma_method()
|
|
|
|
pe.covariance([obs1, obs2])
|
|
|
|
|
|
def test_correlation_intersection_of_idls():
|
|
range1 = range(1, 2000, 2)
|
|
range2 = range(2, 2001, 2)
|
|
|
|
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
|
|
obs2_a = 0.4 * pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1]) + 0.6 * obs1
|
|
obs1.gamma_method()
|
|
obs2_a.gamma_method()
|
|
|
|
cov1 = pe.covariance([obs1, obs2_a])
|
|
corr1 = pe.covariance([obs1, obs2_a], correlation=True)
|
|
|
|
obs2_b = (obs2_a + pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])) / 2
|
|
obs2_b.gamma_method()
|
|
|
|
cov2 = pe.covariance([obs1, obs2_b])
|
|
corr2 = pe.covariance([obs1, obs2_b], correlation=True)
|
|
|
|
assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
|
|
assert cov1[0, 1] > cov2[0, 1]
|
|
|
|
obs2_c = pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
|
|
obs2_c.gamma_method()
|
|
assert np.isclose(0, pe.covariance([obs1, obs2_c])[0, 1], atol=1e-14)
|
|
|
|
|
|
def test_covariance_non_identical_objects():
|
|
obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 732)], ["ens|r1", "ens|r2", "ens2"])
|
|
obs1.gamma_method()
|
|
obs2 = obs1 + 1e-18
|
|
obs2.gamma_method()
|
|
assert obs1 == obs2
|
|
assert obs1 is not obs2
|
|
assert np.allclose(np.ones((2, 2)), pe.covariance([obs1, obs2], correlation=True), atol=1e-14)
|
|
|
|
|
|
def test_covariance_additional_non_overlapping_data():
|
|
range1 = range(1, 20, 2)
|
|
|
|
data2 = np.random.normal(0.0, 0.1, len(range1))
|
|
|
|
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
|
|
obs2_a = pe.Obs([data2], ["ens"], idl=[range1])
|
|
obs1.gamma_method()
|
|
obs2_a.gamma_method()
|
|
|
|
corr1 = pe.covariance([obs1, obs2_a], correlation=True)
|
|
|
|
added_data = np.random.normal(0.0, 0.1, len(range1))
|
|
added_data -= np.mean(added_data) - np.mean(data2)
|
|
data2_extended = np.ravel([data2, added_data], 'F')
|
|
|
|
obs2_b = pe.Obs([data2_extended], ["ens"])
|
|
obs2_b.gamma_method()
|
|
|
|
corr2 = pe.covariance([obs1, obs2_b], correlation=True)
|
|
|
|
assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
|
|
|
|
|
|
def test_covariance_reorder_non_overlapping_data():
|
|
range1 = range(1, 20, 2)
|
|
range2 = range(1, 41, 2)
|
|
|
|
obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
|
|
obs2_b = pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
|
|
obs1.gamma_method()
|
|
obs2_b.gamma_method()
|
|
|
|
corr1 = pe.covariance([obs1, obs2_b], correlation=True)
|
|
|
|
deltas = list(obs2_b.deltas['ens'][:len(range1)]) + sorted(obs2_b.deltas['ens'][len(range1):])
|
|
obs2_a = pe.Obs([obs2_b.value + np.array(deltas)], ["ens"], idl=[range2])
|
|
obs2_a.gamma_method()
|
|
|
|
corr2 = pe.covariance([obs1, obs2_a], correlation=True)
|
|
|
|
assert np.isclose(corr1[0, 1], corr2[0, 1], atol=1e-14)
|
|
|
|
|
|
def test_empty_obs():
|
|
o = pe.Obs([np.random.rand(100)], ['test'])
|
|
q = o + pe.Obs([], [], means=[])
|
|
assert q == o
|
|
|
|
|
|
def test_reweight_method():
|
|
obs1 = pe.pseudo_Obs(0.2, 0.01, 'test')
|
|
rw = pe.pseudo_Obs(0.999, 0.001, 'test')
|
|
assert obs1.reweight(rw) == pe.reweight(rw, [obs1])[0]
|
|
|
|
|
|
def test_jackknife():
|
|
full_data = np.random.normal(1.1, 0.87, 5487)
|
|
|
|
my_obs = pe.Obs([full_data], ['test'])
|
|
|
|
n = full_data.size
|
|
mean = np.mean(full_data)
|
|
tmp_jacks = np.zeros(n + 1)
|
|
tmp_jacks[0] = mean
|
|
for i in range(n):
|
|
tmp_jacks[i + 1] = (n * mean - full_data[i]) / (n - 1)
|
|
|
|
assert np.allclose(tmp_jacks, my_obs.export_jackknife())
|
|
my_new_obs = my_obs + pe.Obs([full_data], ['test2'])
|
|
with pytest.raises(Exception):
|
|
my_new_obs.export_jackknife()
|
|
|
|
|
|
def test_import_jackknife():
|
|
full_data = np.random.normal(1.105, 0.021, 754)
|
|
my_obs = pe.Obs([full_data], ['test'])
|
|
my_jacks = my_obs.export_jackknife()
|
|
reconstructed_obs = pe.import_jackknife(my_jacks, 'test')
|
|
assert my_obs == reconstructed_obs
|
|
|
|
|
|
def test_reduce_deltas():
|
|
idx_old = range(1, 101)
|
|
deltas = [float(i) for i in idx_old]
|
|
idl = [
|
|
range(2, 26, 2),
|
|
range(1, 101),
|
|
np.arange(1, 101),
|
|
[1, 2, 3, 5, 6, 7, 9, 12],
|
|
[7],
|
|
]
|
|
for idx_new in idl:
|
|
new = pe.obs._reduce_deltas(deltas, idx_old, idx_new)
|
|
print(new)
|
|
assert(np.all([float(i) for i in idx_new] == new))
|
|
|
|
|
|
def test_cobs_array():
|
|
cobs = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t']) * (1 + 2j)
|
|
np.identity(4) + cobs
|
|
cobs + np.identity(4)
|
|
np.identity(4) - cobs
|
|
cobs - np.identity(4)
|
|
np.identity(4) * cobs
|
|
cobs * np.identity(4)
|
|
np.identity(4) / cobs
|
|
cobs / np.ones((4, 4))
|
|
|
|
|
|
def test_details_tau_no_error():
|
|
tt = pe.Obs([np.random.rand(500)], ["ens"])
|
|
tt.gamma_method(S=0)
|
|
tt.details()
|
|
|
|
|
|
def test_hash():
|
|
obs = pe.pseudo_Obs(0.3, 0.1, "test") + pe.Obs([np.random.normal(2.3, 0.2, 200)], ["test2"], [range(1, 400, 2)])
|
|
o1 = obs + pe.cov_Obs(0.0, 0.1, "co") + pe.cov_Obs(0.0, 0.8, "co2")
|
|
o2 = obs + pe.cov_Obs(0.0, 0.2, "co") + pe.cov_Obs(0.0, 0.8, "co2")
|
|
|
|
for i_obs in [obs, o1, o2]:
|
|
assert hash(i_obs) == hash(i_obs ** 2 / i_obs) == hash(1 * i_obs)
|
|
assert hash(i_obs) == hash((1 + 1e-16) * i_obs)
|
|
assert hash(i_obs) != hash((1 + 1e-7) * i_obs)
|
|
assert hash(obs) != hash(o1)
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assert hash(o1) != hash(o2)
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|
|
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def test_gm_alias():
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samples = np.random.rand(500)
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tt1 = pe.Obs([samples], ["ens"])
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tt1.gamma_method()
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|
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tt2 = pe.Obs([samples], ["ens"])
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tt2.gm()
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|
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assert np.isclose(tt1.dvalue, tt2.dvalue)
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|
|
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def test_overlapping_missing_cnfgs():
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length = 200000
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|
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l_samp = np.random.normal(2.87, 0.5, length)
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s_samp = np.random.normal(7.87, 0.7, length // 2)
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|
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o1 = pe.Obs([l_samp], ["test"])
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o2 = pe.Obs([s_samp], ["test"], idl=[range(1, length, 2)])
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|
|
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a2 = pe.Obs([s_samp], ["alt"])
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t1 = o1 + o2
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t1.gm(S=0)
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|
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t2 = o1 + a2
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t2.gm(S=0)
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assert np.isclose(t1.value, t2.value)
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assert np.isclose(t1.dvalue, t2.dvalue, rtol=0.01)
|
|
|
|
|
|
def test_non_overlapping_missing_cnfgs():
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length = 100000
|
|
|
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xsamp = np.random.normal(1.0, 1.0, length)
|
|
|
|
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full = pe.Obs([xsamp], ["ensemble"], idl=[range(0, length)])
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|
full.gm()
|
|
|
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even = pe.Obs([xsamp[0:length:2]], ["ensemble"], idl=[range(0, length, 2)])
|
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odd = pe.Obs([xsamp[1:length:2]], ["ensemble"], idl=[range(1, length, 2)])
|
|
|
|
average = (even + odd) / 2
|
|
average.gm(S=0)
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|
assert np.isclose(full.value, average.value)
|
|
assert np.isclose(full.dvalue, average.dvalue, rtol=0.02)
|
|
|
|
|
|
def test_non_overlapping_operations():
|
|
length = 100000
|
|
|
|
samples = np.random.normal(0.93, 0.5, length)
|
|
|
|
e = pe.Obs([samples[0:length:2]], ["ensemble"], idl=[range(0, length, 2)])
|
|
o = pe.Obs([samples[1:length:2]], ["ensemble"], idl=[range(1, length, 2)])
|
|
|
|
|
|
e2 = pe.Obs([samples[0:length:2]], ["even"])
|
|
o2 = pe.Obs([samples[1:length:2]], ["odd"])
|
|
|
|
for func in [lambda a, b: a + b,
|
|
lambda a, b: a - b,
|
|
lambda a, b: a * b,
|
|
lambda a, b: a / b,
|
|
lambda a, b: a ** b]:
|
|
|
|
res1 = func(e, o)
|
|
res1.gm(S=0)
|
|
res2 = func(e2, o2)
|
|
res2.gm(S=0)
|
|
|
|
print(res1, res2)
|
|
print((res1.dvalue - res2.dvalue) / res1.dvalue)
|
|
|
|
assert np.isclose(res1.value, res2.value)
|
|
assert np.isclose(res1.dvalue, res2.dvalue, rtol=0.01)
|
|
|
|
|
|
def test_non_overlapping_operations_different_lengths():
|
|
length = 100000
|
|
|
|
samples = np.random.normal(0.93, 0.5, length)
|
|
first = samples[:length // 5]
|
|
second = samples[length // 5:]
|
|
|
|
f1 = pe.Obs([first], ["ensemble"], idl=[range(1, length // 5 + 1)])
|
|
s1 = pe.Obs([second], ["ensemble"], idl=[range(length // 5 + 1, length + 1)])
|
|
|
|
|
|
f2 = pe.Obs([first], ["first"])
|
|
s2 = pe.Obs([second], ["second"])
|
|
|
|
for func in [lambda a, b: a + b,
|
|
lambda a, b: a - b,
|
|
lambda a, b: a * b,
|
|
lambda a, b: a / b,
|
|
lambda a, b: a ** b,
|
|
lambda a, b: a ** 2 + b ** 2 / a]:
|
|
|
|
res1 = func(f1, s1)
|
|
res1.gm(S=0)
|
|
res2 = func(f2, s2)
|
|
res2.gm(S=0)
|
|
|
|
assert np.isclose(res1.value, res2.value)
|
|
assert np.isclose(res1.dvalue, res2.dvalue, rtol=0.01)
|
|
|
|
|
|
def test_nan_obs():
|
|
o = pe.pseudo_Obs(1, .1, 'test')
|
|
no = np.nan * o
|
|
no.gamma_method()
|
|
|
|
o.idl['test'] = [1, 5] + list(range(7, 2002, 2))
|
|
no = np.NaN * o
|
|
no.gamma_method()
|
|
|
|
|
|
def test_format_uncertainty():
|
|
assert pe.obs._format_uncertainty(0.548, 0.248976, 4) == '0.5480(2490)'
|
|
assert pe.obs._format_uncertainty(0.548, 2.48497, 2) == '0.5(2.5)'
|
|
assert pe.obs._format_uncertainty(0.548, 2.48497, 4) == '0.548(2.485)'
|
|
assert pe.obs._format_uncertainty(0.548, 20078.3, 9) == '0.5480(20078.3000)'
|
|
pe.obs._format_uncertainty(np.NaN, 1)
|
|
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}")
|
|
print(f"{o1:3}")
|
|
print(f"{o1:+3}")
|
|
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)
|