mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 14:50:25 +01:00
Merge branch 'develop' into feature/covobs
This commit is contained in:
Simon Kuberski
commit
9db709a171
9 changed files with 135 additions and 176 deletions
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@ -246,12 +246,11 @@ See `pyerrors.obs.Obs.export_jackknife` for details.
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from .obs import *
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from .correlators import *
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from .fits import *
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from .misc import *
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from . import dirac
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from . import input
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from . import linalg
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from . import misc
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from . import mpm
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from . import npr
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from . import roots
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from .version import __version__
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@ -3,7 +3,8 @@ import numpy as np
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import autograd.numpy as anp
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import matplotlib.pyplot as plt
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import scipy.linalg
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from .obs import Obs, dump_object, reweight, correlate
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from .obs import Obs, reweight, correlate
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from .misc import dump_object
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from .fits import least_squares
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from .linalg import eigh, inv, cholesky
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from .roots import find_root
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@ -301,7 +301,8 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
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result = []
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for i in range(n_parms):
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result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(out.beta[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(x.ravel()) + list(y), man_grad=[0] + list(deriv_x[i]) + list(deriv_y[i])))
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result.append(derived_observable(lambda x, **kwargs: x[0], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
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result[-1]._value = out.beta[i]
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output.fit_parameters = result + const_par
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@ -418,7 +419,8 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
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result = []
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for i in range(n_parms):
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result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(params[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(y) + list(loc_priors), man_grad=[0] + list(deriv[i])))
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result.append(derived_observable(lambda x, **kwargs: x[0], list(y) + list(loc_priors), man_grad=list(deriv[i])))
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result[-1]._value = params[i]
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output.fit_parameters = result
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output.chisquare = chisqfunc(np.asarray(params))
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@ -612,7 +614,8 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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result = []
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for i in range(n_parms):
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result.append(derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(fit_result.x[i], 0.0, y[0].names[0], y[0].shape[y[0].names[0]])] + list(y), man_grad=[0] + list(deriv[i])))
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result.append(derived_observable(lambda x, **kwargs: x[0], list(y), man_grad=list(deriv[i])))
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result[-1]._value = fit_result.x[i]
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output.fit_parameters = result + const_par
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@ -1,15 +1,11 @@
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#!/usr/bin/env python
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# coding: utf-8
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import os
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import h5py
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import numpy as np
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from ..obs import Obs, CObs
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from ..correlators import Corr
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from ..npr import Npr_matrix
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def _get_files(path, filestem):
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def _get_files(path, filestem, idl):
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ls = os.listdir(path)
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# Clean up file list
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@ -24,11 +20,19 @@ def _get_files(path, filestem):
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# Sort according to configuration number
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files.sort(key=get_cnfg_number)
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# Check that configurations are evenly spaced
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cnfg_numbers = []
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filtered_files = []
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for line in files:
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cnfg_numbers.append(get_cnfg_number(line))
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no = get_cnfg_number(line)
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if idl:
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if no in list(idl):
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filtered_files.append(line)
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cnfg_numbers.append(no)
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else:
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filtered_files.append(line)
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cnfg_numbers.append(no)
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# Check that configurations are evenly spaced
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dc = np.unique(np.diff(cnfg_numbers))
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if np.any(dc < 0):
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raise Exception("Unsorted files")
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@ -37,10 +41,10 @@ def _get_files(path, filestem):
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else:
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raise Exception('Configurations are not evenly spaced.')
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return files, idx
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return filtered_files, idx
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def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson'):
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def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson', idl=None):
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"""Read hadrons meson hdf5 file and extract the meson labeled 'meson'
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Parameters
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@ -58,9 +62,11 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson'):
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Label of the upmost directory in the hdf5 file, default 'meson'
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for outputs of the Meson module. Can be altered to read input
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from other modules with similar structures.
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idl : range
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If specified only conifgurations in the given range are read in.
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"""
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files, idx = _get_files(path, filestem)
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files, idx = _get_files(path, filestem, idl)
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corr_data = []
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infos = []
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@ -84,7 +90,47 @@ def read_meson_hd5(path, filestem, ens_id, meson='meson_0', tree='meson'):
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return corr
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def read_ExternalLeg_hd5(path, filestem, ens_id, order='F'):
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class Npr_matrix(np.ndarray):
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def __new__(cls, input_array, mom_in=None, mom_out=None):
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obj = np.asarray(input_array).view(cls)
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obj.mom_in = mom_in
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obj.mom_out = mom_out
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return obj
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@property
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def g5H(self):
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"""Gamma_5 hermitean conjugate
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Uses the fact that the propagator is gamma5 hermitean, so just the
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in and out momenta of the propagator are exchanged.
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"""
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return Npr_matrix(self,
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mom_in=self.mom_out,
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mom_out=self.mom_in)
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def _propagate_mom(self, other, name):
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s_mom = getattr(self, name, None)
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o_mom = getattr(other, name, None)
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if s_mom is not None and o_mom is not None:
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if not np.allclose(s_mom, o_mom):
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raise Exception(name + ' does not match.')
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return o_mom if o_mom is not None else s_mom
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def __matmul__(self, other):
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return self.__new__(Npr_matrix,
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super().__matmul__(other),
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self._propagate_mom(other, 'mom_in'),
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self._propagate_mom(other, 'mom_out'))
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def __array_finalize__(self, obj):
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if obj is None:
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return
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self.mom_in = getattr(obj, 'mom_in', None)
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self.mom_out = getattr(obj, 'mom_out', None)
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def read_ExternalLeg_hd5(path, filestem, ens_id, idl=None):
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"""Read hadrons ExternalLeg hdf5 file and output an array of CObs
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Parameters
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@ -92,12 +138,11 @@ def read_ExternalLeg_hd5(path, filestem, ens_id, order='F'):
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path -- path to the files to read
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filestem -- namestem of the files to read
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ens_id -- name of the ensemble, required for internal bookkeeping
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order -- order in which the array is to be reshaped,
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'F' for the first index changing fastest (9 4x4 matrices) default.
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'C' for the last index changing fastest (16 3x3 matrices),
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idl : range
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If specified only conifgurations in the given range are read in.
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"""
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files, idx = _get_files(path, filestem)
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files, idx = _get_files(path, filestem, idl)
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mom = None
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@ -119,10 +164,10 @@ def read_ExternalLeg_hd5(path, filestem, ens_id, order='F'):
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imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
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matrix[si, sj, ci, cj] = CObs(real, imag)
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return Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order=order), mom_in=mom)
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return Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order='F'), mom_in=mom)
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def read_Bilinear_hd5(path, filestem, ens_id, order='F'):
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def read_Bilinear_hd5(path, filestem, ens_id, idl=None):
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"""Read hadrons Bilinear hdf5 file and output an array of CObs
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Parameters
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@ -130,12 +175,11 @@ def read_Bilinear_hd5(path, filestem, ens_id, order='F'):
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path -- path to the files to read
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filestem -- namestem of the files to read
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ens_id -- name of the ensemble, required for internal bookkeeping
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order -- order in which the array is to be reshaped,
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'F' for the first index changing fastest (9 4x4 matrices) default.
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'C' for the last index changing fastest (16 3x3 matrices),
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idl : range
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If specified only conifgurations in the given range are read in.
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"""
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files, idx = _get_files(path, filestem)
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files, idx = _get_files(path, filestem, idl)
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mom_in = None
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mom_out = None
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@ -169,6 +213,6 @@ def read_Bilinear_hd5(path, filestem, ens_id, order='F'):
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imag = Obs([rolled_array[si, sj, ci, cj].imag], [ens_id], idl=[idx])
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matrix[si, sj, ci, cj] = CObs(real, imag)
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result_dict[key] = Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order=order), mom_in=mom_in, mom_out=mom_out)
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result_dict[key] = Npr_matrix(matrix.swapaxes(1, 2).reshape((12, 12), order='F'), mom_in=mom_in, mom_out=mom_out)
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return result_dict
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@ -1,18 +1,56 @@
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import pickle
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import numpy as np
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from .obs import Obs
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def dump_object(obj, name, **kwargs):
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"""Dump object into pickle file.
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Parameters
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----------
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obj : object
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object to be saved in the pickle file
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name : str
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name of the file
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path : str
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specifies a custom path for the file (default '.')
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"""
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if 'path' in kwargs:
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file_name = kwargs.get('path') + '/' + name + '.p'
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else:
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file_name = name + '.p'
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with open(file_name, 'wb') as fb:
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pickle.dump(obj, fb)
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def load_object(path):
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"""Load object from pickle file.
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Parameters
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----------
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path : str
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path to the file
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"""
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with open(path, 'rb') as file:
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return pickle.load(file)
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def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
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""" Generate observables with given covariance and autocorrelation times.
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Parameters
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----------
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means -- list containing the mean value of each observable.
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cov -- covariance matrix for the data to be geneated.
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name -- ensemble name for the data to be geneated.
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tau -- can either be a real number or a list with an entry for
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every dataset.
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samples -- number of samples to be generated for each observable.
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means : list
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list containing the mean value of each observable.
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cov : numpy.ndarray
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covariance matrix for the data to be generated.
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name : str
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ensemble name for the data to be geneated.
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tau : float or list
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can either be a real number or a list with an entry for
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every dataset.
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samples : int
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number of samples to be generated for each observable.
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"""
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assert len(means) == cov.shape[-1]
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108
pyerrors/npr.py
108
pyerrors/npr.py
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@ -1,108 +0,0 @@
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import warnings
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import numpy as np
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from .linalg import inv, matmul
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from .dirac import gamma
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L = None
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T = None
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class Npr_matrix(np.ndarray):
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def __new__(cls, input_array, mom_in=None, mom_out=None):
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obj = np.asarray(input_array).view(cls)
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obj.mom_in = mom_in
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obj.mom_out = mom_out
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return obj
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@property
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def g5H(self):
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"""Gamma_5 hermitean conjugate
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Uses the fact that the propagator is gamma5 hermitean, so just the
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in and out momenta of the propagator are exchanged.
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"""
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return Npr_matrix(self,
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mom_in=self.mom_out,
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mom_out=self.mom_in)
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def _propagate_mom(self, other, name):
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s_mom = getattr(self, name, None)
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o_mom = getattr(other, name, None)
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if s_mom is not None and o_mom is not None:
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if not np.allclose(s_mom, o_mom):
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raise Exception(name + ' does not match.')
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return o_mom if o_mom is not None else s_mom
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def __matmul__(self, other):
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return self.__new__(Npr_matrix,
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super().__matmul__(other),
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self._propagate_mom(other, 'mom_in'),
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self._propagate_mom(other, 'mom_out'))
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def __array_finalize__(self, obj):
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if obj is None:
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return
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self.mom_in = getattr(obj, 'mom_in', None)
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self.mom_out = getattr(obj, 'mom_out', None)
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def _check_geometry():
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if L is None:
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raise Exception("Spatial extent 'L' not set.")
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else:
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if not isinstance(L, int):
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raise Exception("Spatial extent 'L' must be an integer.")
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if T is None:
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raise Exception("Temporal extent 'T' not set.")
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if not isinstance(T, int):
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raise Exception("Temporal extent 'T' must be an integer.")
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def inv_propagator(prop):
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""" Inverts a 12x12 quark propagator"""
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if prop.shape != (12, 12):
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raise Exception("Only 12x12 propagators can be inverted.")
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return Npr_matrix(inv(prop), prop.mom_in)
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def Zq(inv_prop, fermion='Wilson'):
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""" Calculates the quark field renormalization constant Zq
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Parameters
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----------
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inv_prop : array
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Inverted 12x12 quark propagator
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fermion : str
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Fermion type for which the tree-level propagator is used
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in the calculation of Zq. Default Wilson.
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"""
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_check_geometry()
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mom = np.copy(inv_prop.mom_in)
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mom[3] /= T / L
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sin_mom = np.sin(2 * np.pi / L * mom)
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if fermion == 'Wilson':
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p_slash = -1j * (sin_mom[0] * gamma[0] + sin_mom[1] * gamma[1] + sin_mom[2] * gamma[2] + sin_mom[3] * gamma[3]) / np.sum(sin_mom ** 2)
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elif fermion == 'Continuum':
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p_mom = 2 * np.pi / L * mom
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p_slash = -1j * (p_mom[0] * gamma[0] + p_mom[1] * gamma[1] + p_mom[2] * gamma[2] + p_mom[3] * gamma[3]) / np.sum(p_mom ** 2)
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elif fermion == 'DWF':
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W = np.sum(1 - np.cos(2 * np.pi / L * mom))
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s2 = np.sum(sin_mom ** 2)
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p_slash = -1j * (sin_mom[0] * gamma[0] + sin_mom[1] * gamma[1] + sin_mom[2] * gamma[2] + sin_mom[3] * gamma[3])
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p_slash /= 2 * (W - 1 + np.sqrt((1 - W) ** 2 + s2))
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else:
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raise Exception("Fermion type '" + fermion + "' not implemented")
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res = 1 / 12. * np.trace(matmul(inv_prop, np.kron(np.eye(3, dtype=int), p_slash)))
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res.gamma_method()
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if not res.imag.is_zero_within_error(5):
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warnings.warn("Imaginary part of Zq is not zero within 5 sigma")
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return res
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res.real.tag = "Zq '" + fermion + "', p=" + str(inv_prop.mom_in)
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return res.real
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|
@ -1578,38 +1578,6 @@ def pseudo_Obs(value, dvalue, name, samples=1000):
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return res
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def dump_object(obj, name, **kwargs):
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"""Dump object into pickle file.
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|
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Parameters
|
||||
----------
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||||
obj : object
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||||
object to be saved in the pickle file
|
||||
name : str
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||||
name of the file
|
||||
path : str
|
||||
specifies a custom path for the file (default '.')
|
||||
"""
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if 'path' in kwargs:
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file_name = kwargs.get('path') + '/' + name + '.p'
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else:
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file_name = name + '.p'
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with open(file_name, 'wb') as fb:
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pickle.dump(obj, fb)
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||||
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def load_object(path):
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"""Load object from pickle file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : str
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||||
path to the file
|
||||
"""
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||||
with open(path, 'rb') as file:
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return pickle.load(file)
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||||
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||||
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||||
def import_jackknife(jacks, name, idl=None):
|
||||
"""Imports jackknife samples and returns an Obs
|
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|
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@ -1,6 +1,6 @@
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import scipy.optimize
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from autograd import jacobian
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from .obs import derived_observable, pseudo_Obs
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from .obs import derived_observable
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def find_root(d, func, guess=1.0, **kwargs):
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@ -33,4 +33,6 @@ def find_root(d, func, guess=1.0, **kwargs):
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da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
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deriv = - da / dx
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return derived_observable(lambda x, **kwargs: x[0], [pseudo_Obs(root, 0.0, d.names[0], d.shape[d.names[0]]), d], man_grad=[0, deriv])
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res = derived_observable(lambda x, **kwargs: x[0], [d], man_grad=[deriv])
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res._value = root[0]
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return res
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|
|
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@ -17,3 +17,15 @@ def test_root_linear():
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assert np.isclose(my_root.value, value)
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difference = my_obs - my_root
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assert difference.is_zero()
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def test_root_linear_idl():
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def root_function(x, d):
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return x - d
|
||||
|
||||
my_obs = pe.Obs([np.random.rand(50)], ['t'], idl=[range(20, 120, 2)])
|
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my_root = pe.roots.find_root(my_obs, root_function)
|
||||
|
||||
difference = my_obs - my_root
|
||||
assert difference.is_zero()
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue