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jackknifing module removed
This commit is contained in:
Fabian Joswig
parent
9bd438fa06
commit
f1394bbde6
5 changed files with 11 additions and 227 deletions
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@ -1,155 +0,0 @@
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#!/usr/bin/env python
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# coding: utf-8
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import pickle
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import matplotlib.pyplot as plt
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import numpy as np
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def _jack_error(jack):
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n = jack.size
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mean = np.mean(jack)
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error = 0
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for i in range(n):
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error += (jack[i] - mean) ** 2
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return np.sqrt((n - 1) / n * error)
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class Jack:
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def __init__(self, value, jacks):
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self.jacks = jacks
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self.N = list(map(np.size, self.jacks))
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self.max_binsize = len(self.N)
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self.value = value # list(map(np.mean, self.jacks))
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self.dvalue = list(map(_jack_error, self.jacks))
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def print(self, **kwargs):
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"""Print basic properties of the Jack."""
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if 'binsize' in kwargs:
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b = kwargs.get('binsize') - 1
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if b == -1:
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b = 0
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if not isinstance(b, int):
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raise TypeError('binsize has to be integer')
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if b + 1 > self.max_binsize:
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raise Exception('Chosen binsize not calculated')
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else:
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b = 0
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print('Result:\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self.dvalue[b], self.dvalue[b] * np.sqrt(2 * b / self.N[0]), np.abs(self.dvalue[b] / self.value * 100)))
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def plot_tauint(self):
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plt.xlabel('binsize')
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plt.ylabel('tauint')
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length = self.max_binsize
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x = np.arange(length) + 1
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plt.errorbar(x[:], (self.dvalue[:] / self.dvalue[0]) ** 2 / 2, yerr=np.sqrt(((2 * (self.dvalue[:] / self.dvalue[0]) ** 2 * np.sqrt(2 * x[:] / self.N[0])) / 2) ** 2 + ((2 * (self.dvalue[:] / self.dvalue[0]) ** 2 * np.sqrt(2 / self.N[0])) / 2) ** 2), linewidth=1, capsize=2)
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plt.xlim(0.5, length + 0.5)
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plt.title('Tauint')
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plt.show()
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def plot_history(self):
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N = self.N
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x = np.arange(N)
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tmp = []
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for i in range(self.replicas):
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tmp.append(self.deltas[i] + self.r_values[i])
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y = np.concatenate(tmp, axis=0) # Think about including kwarg to look only at some replica
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plt.errorbar(x, y, fmt='.', markersize=3)
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plt.xlim(-0.5, N - 0.5)
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plt.show()
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def dump(self, name, **kwargs):
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"""Dump the Jack to a pickle file 'name'.
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Keyword arguments:
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path -- 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(self, fb)
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def generate_jack(obs, **kwargs):
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full_data = []
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for r, name in enumerate(obs.names):
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if r == 0:
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full_data = obs.deltas[name] + obs.r_values[name]
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else:
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full_data = np.append(full_data, obs.deltas[name] + obs.r_values[name])
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jacks = []
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if 'max_binsize' in kwargs:
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max_b = kwargs.get('max_binsize')
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if not isinstance(max_b, int):
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raise TypeError('max_binsize has to be integer')
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else:
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max_b = 1
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for b in range(max_b):
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# binning if necessary
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if b > 0:
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n = full_data.size // (b + 1)
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binned_data = np.zeros(n)
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for i in range(n):
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for j in range(b + 1):
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binned_data[i] += full_data[i * (b + 1) + j]
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binned_data[i] /= (b + 1)
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else:
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binned_data = full_data
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n = binned_data.size
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# generate jacks from data
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mean = np.mean(binned_data)
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tmp_jacks = np.zeros(n)
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for i in range(n):
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tmp_jacks[i] = (n * mean - binned_data[i]) / (n - 1)
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jacks.append(tmp_jacks)
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# Value is not correctly reproduced here
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return Jack(obs.value, jacks)
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def derived_jack(func, data, **kwargs):
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"""Construct a derived Jack according to func(data, **kwargs).
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Parameters
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----------
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func -- arbitrary function of the form func(data, **kwargs). For the automatic differentiation to work,
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all numpy functions have to have the autograd wrapper (use 'import autograd.numpy as np').
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data -- list of Jacks, e.g. [jack1, jack2, jack3].
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Notes
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-----
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For simple mathematical operations it can be practical to use anonymous functions.
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For the ratio of two jacks one can e.g. use
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new_jack = derived_jack(lambda x : x[0] / x[1], [jack1, jack2])
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"""
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# Check shapes of data
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if not all(x.N == data[0].N for x in data):
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raise Exception('Error: Shape of data does not fit')
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values = np.zeros(len(data))
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for j, item in enumerate(data):
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values[j] = item.value
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new_value = func(values, **kwargs)
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jacks = []
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for b in range(data[0].max_binsize):
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tmp_jacks = np.zeros(data[0].N[b])
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for i in range(data[0].N[b]):
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values = np.zeros(len(data))
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for j, item in enumerate(data):
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values[j] = item.jacks[b][i]
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tmp_jacks[i] = func(values, **kwargs)
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jacks.append(tmp_jacks)
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return Jack(new_value, jacks)
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@ -1,6 +1,3 @@
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#!/usr/bin/env python
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# coding: utf-8
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import numpy as np
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from autograd import jacobian
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import autograd.numpy as anp # Thinly-wrapped numpy
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@ -1,6 +1,3 @@
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#!/usr/bin/env python
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# coding: utf-8
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import numpy as np
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from .obs import Obs
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@ -1,26 +1,26 @@
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#!/usr/bin/env python
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# coding: utf-8
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import numpy as np
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import scipy.linalg
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from .obs import Obs
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from .linalg import svd, eig, pinv
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from .linalg import svd, eig
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def matrix_pencil_method(corrs, k=1, p=None, **kwargs):
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""" Matrix pencil method to extract k energy levels from data
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"""Matrix pencil method to extract k energy levels from data
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Implementation of the matrix pencil method based on
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eq. (2.17) of Y. Hua, T. K. Sarkar, IEEE Trans. Acoust. 38, 814-824 (1990)
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Parameters
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----------
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data -- can be a list of Obs for the analysis of a single correlator, or a list of lists
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of Obs if several correlators are to analyzed at once.
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k -- Number of states to extract (default 1).
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p -- matrix pencil parameter which filters noise. The optimal value is expected between
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len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is
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to len(data)/2 but could possibly suppress more noise (default len(data)//2).
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data : list
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can be a list of Obs for the analysis of a single correlator, or a list of lists
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of Obs if several correlators are to analyzed at once.
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k : int
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Number of states to extract (default 1).
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p : int
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matrix pencil parameter which filters noise. The optimal value is expected between
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len(data)/3 and 2*len(data)/3. The computation is more expensive the closer p is
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to len(data)/2 but could possibly suppress more noise (default len(data)//2).
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"""
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if isinstance(corrs[0], Obs):
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data = [corrs]
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@ -56,55 +56,3 @@ def matrix_pencil_method(corrs, k=1, p=None, **kwargs):
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# Return the sorted logarithms of the real eigenvalues as Obs
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energy_levels = np.log(np.abs(eig(z, **kwargs)))
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return sorted(energy_levels, key=lambda x: abs(x.value))
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def matrix_pencil_method_old(data, p, noise_level=None, verbose=1, **kwargs):
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""" Older impleentation of the matrix pencil method with pencil p on given data to
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extract energy levels.
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Parameters
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----------
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data -- lists of Obs, where the nth entry is considered to be the correlation function
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at x0=n+offset.
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p -- matrix pencil parameter which corresponds to the number of energy levels to extract.
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higher values for p can help decreasing noise.
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noise_level -- If this argument is not None an additional prefiltering via singular
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value decomposition is performed in which all singular values below 10^(-noise_level)
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times the largest singular value are discarded. This increases the computation time.
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verbose -- if larger than zero details about the noise filtering are printed to stdout
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(default 1)
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"""
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n_data = len(data)
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if n_data <= p:
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raise Exception('The pencil p has to be smaller than the number of data samples.')
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matrix = scipy.linalg.hankel(data[:n_data - p], data[n_data - p - 1:]) @ np.identity(p + 1)
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if noise_level is not None:
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u, s, vh = svd(matrix)
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s_values = np.vectorize(lambda x: x.value)(s)
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if verbose > 0:
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print('Singular values: ', s_values)
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digit = np.argwhere(s_values / s_values[0] < 10.0**(-noise_level))
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if digit.size == 0:
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digit = len(s_values)
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else:
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digit = int(digit[0])
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if verbose > 0:
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print('Consider only', digit, 'out of', len(s), 'singular values')
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new_matrix = u[:, :digit] * s[:digit] @ vh[:digit, :]
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y1 = new_matrix[:, :-1]
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y2 = new_matrix[:, 1:]
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else:
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y1 = matrix[:, :-1]
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y2 = matrix[:, 1:]
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# Moore–Penrose pseudoinverse
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pinv_y1 = pinv(y1)
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e = eig((pinv_y1 @ y2), **kwargs)
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energy_levels = -np.log(np.abs(e))
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return sorted(energy_levels, key=lambda x: abs(x.value))
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@ -1,6 +1,3 @@
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#!/usr/bin/env python
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# coding: utf-8
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import warnings
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import pickle
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import numpy as np
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