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first version of new bookeeping system
This commit is contained in:
Fabian Joswig
parent
b86dda2394
commit
e4d7d56180
4 changed files with 38 additions and 54 deletions
2
.github/workflows/pytest.yml
vendored
2
.github/workflows/pytest.yml
vendored
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@ -33,4 +33,4 @@ jobs:
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pip install pytest-benchmark
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- name: Run tests
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run: pytest --cov=pyerrors -v
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run: pytest --cov=pyerrors -vv
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@ -24,9 +24,6 @@ class Obs:
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Attributes
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----------
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e_tag_global : int
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Integer which determines which part of the name belongs
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to the ensemble and which to the replicum.
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S_global : float
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Standard value for S (default 2.0)
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S_dict : dict
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@ -41,12 +38,11 @@ class Obs:
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Standard value for N_sigma (default 1.0)
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"""
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__slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
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'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma', 'e_names',
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'e_content', 'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
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'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
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'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
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'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
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'idl', 'is_merged', 'tag', '__dict__']
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e_tag_global = 0
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S_global = 2.0
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S_dict = {}
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tau_exp_global = 0.0
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@ -141,6 +137,19 @@ class Obs:
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def dvalue(self):
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return self._dvalue
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@property
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def e_names(self):
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return sorted(set([o.split('|')[0] for o in self.names]))
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@property
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def e_content(self):
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res = {}
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for e, e_name in enumerate(self.e_names):
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res[e_name] = sorted(filter(lambda x: x.startswith(e_name + '|'), self.names))
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if e_name in self.names:
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res[e_name].append(e_name)
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return res
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def expand_deltas(self, deltas, idx, shape):
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"""Expand deltas defined on idx to a regular, contiguous range, where holes are filled by 0.
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If idx is of type range, the deltas are not changed
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@ -202,23 +211,12 @@ class Obs:
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N_sigma : float
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number of standard deviations from zero until the tail is
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attached to the autocorrelation function (default 1)
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e_tag : int
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number of characters which label the ensemble. The remaining
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ones label replica (default 0)
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fft : bool
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determines whether the fft algorithm is used for the computation
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of the autocorrelation function (default True)
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"""
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if 'e_tag' in kwargs:
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e_tag_local = kwargs.get('e_tag')
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if not isinstance(e_tag_local, int):
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raise TypeError('Error: e_tag is not integer')
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else:
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e_tag_local = Obs.e_tag_global
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self.e_names = sorted(set([o[:e_tag_local] for o in self.names]))
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self.e_content = {}
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e_content = self.e_content
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self.e_dvalue = {}
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self.e_ddvalue = {}
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self.e_tauint = {}
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@ -295,36 +293,26 @@ class Obs:
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else:
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self.N_sigma = Obs.N_sigma_global
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if max([len(x) for x in self.names]) <= e_tag_local:
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for e, e_name in enumerate(self.e_names):
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self.e_content[e_name] = [e_name]
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else:
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for e, e_name in enumerate(self.e_names):
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if len(e_name) < e_tag_local:
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self.e_content[e_name] = [e_name]
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else:
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self.e_content[e_name] = sorted(filter(lambda x: x.startswith(e_name), self.names))
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for e, e_name in enumerate(self.e_names):
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r_length = []
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for r_name in self.e_content[e_name]:
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for r_name in e_content[e_name]:
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if self.idl[r_name] is range:
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r_length.append(len(self.idl[r_name]))
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else:
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r_length.append((self.idl[r_name][-1] - self.idl[r_name][0] + 1))
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e_N = np.sum([self.shape[r_name] for r_name in self.e_content[e_name]])
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e_N = np.sum([self.shape[r_name] for r_name in e_content[e_name]])
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w_max = max(r_length) // 2
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e_gamma[e_name] = np.zeros(w_max)
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self.e_rho[e_name] = np.zeros(w_max)
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self.e_drho[e_name] = np.zeros(w_max)
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for r_name in self.e_content[e_name]:
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for r_name in e_content[e_name]:
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e_gamma[e_name] += self.calc_gamma(self.deltas[r_name], self.idl[r_name], self.shape[r_name], w_max, fft)
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gamma_div = np.zeros(w_max)
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for r_name in self.e_content[e_name]:
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for r_name in e_content[e_name]:
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gamma_div += self.calc_gamma(np.ones((self.shape[r_name])), self.idl[r_name], self.shape[r_name], w_max, fft)
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e_gamma[e_name] /= gamma_div[:w_max]
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@ -384,11 +372,11 @@ class Obs:
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self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
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self._dvalue = np.sqrt(self.dvalue)
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if self.dvalue == 0.0:
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if self._dvalue == 0.0:
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self.ddvalue = 0.0
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else:
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self.ddvalue = np.sqrt(self.ddvalue) / self.dvalue
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return 0
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return
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def print(self, level=1):
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"""Print basic properties of the Obs."""
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@ -400,7 +388,7 @@ class Obs:
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else:
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percentage = np.abs(self.dvalue / self.value) * 100
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print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self.dvalue, self.ddvalue, percentage))
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if hasattr(self, 'e_names'):
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if hasattr(self, 'e_dvalue'):
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if len(self.e_names) > 1:
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print(' Ensemble errors:')
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for e_name in self.e_names:
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@ -30,9 +30,8 @@ def test_least_squares():
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out = pe.least_squares(x, oy, func)
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beta = out.fit_parameters
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pe.Obs.e_tag_global = 5
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for i in range(2):
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beta[i].gamma_method(e_tag=5, S=1.0)
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beta[i].gamma_method(S=1.0)
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assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
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assert math.isclose(pcov[i, i], beta[i].dvalue ** 2, abs_tol=1e-3)
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assert math.isclose(pe.covariance(beta[0], beta[1]), pcov[0, 1], abs_tol=1e-3)
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@ -135,7 +135,7 @@ def test_fft():
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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[''] - test_obs2.e_rho[''])) <= 10 * np.finfo(np.float64).eps
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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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@ -190,22 +190,19 @@ def test_derived_observables():
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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_system():
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names = []
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for i in range(100 + int(np.random.rand() * 50)):
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tmp_string = ''
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for _ in range(int(2 + np.random.rand() * 4)):
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tmp_string += random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits)
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names.append(tmp_string)
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names = list(set(names))
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samples = [np.random.rand(5)] * len(names)
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new_obs = pe.Obs(samples, names)
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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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for e_tag_length in range(1, 6):
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new_obs.gamma_method(e_tag=e_tag_length)
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e_names = sorted(set([n[:e_tag_length] for n in names]))
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assert e_names == new_obs.e_names
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assert sorted(x for y in sorted(new_obs.e_content.values()) for x in y) == sorted(new_obs.names)
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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_overloaded_functions():
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