From 9a198ee31df062a26ada72c36c87cd7bd52fe7f1 Mon Sep 17 00:00:00 2001 From: Fabian Joswig Date: Tue, 9 Nov 2021 10:35:00 +0000 Subject: [PATCH] jackknifing removed --- pyerrors/jackknifing.py | 155 ---------------------------------------- 1 file changed, 155 deletions(-) delete mode 100644 pyerrors/jackknifing.py diff --git a/pyerrors/jackknifing.py b/pyerrors/jackknifing.py deleted file mode 100644 index 2eadba82..00000000 --- a/pyerrors/jackknifing.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python -# coding: utf-8 - -import pickle -import matplotlib.pyplot as plt -import numpy as np - - -def _jack_error(jack): - n = jack.size - mean = np.mean(jack) - error = 0 - for i in range(n): - error += (jack[i] - mean) ** 2 - - return np.sqrt((n - 1) / n * error) - - -class Jack: - - def __init__(self, value, jacks): - self.jacks = jacks - self.N = list(map(np.size, self.jacks)) - self.max_binsize = len(self.N) - self.value = value # list(map(np.mean, self.jacks)) - self.dvalue = list(map(_jack_error, self.jacks)) - - def print(self, **kwargs): - """Print basic properties of the Jack.""" - - if 'binsize' in kwargs: - b = kwargs.get('binsize') - 1 - if b == -1: - b = 0 - if not isinstance(b, int): - raise TypeError('binsize has to be integer') - if b + 1 > self.max_binsize: - raise Exception('Chosen binsize not calculated') - else: - b = 0 - - 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))) - - def plot_tauint(self): - plt.xlabel('binsize') - plt.ylabel('tauint') - length = self.max_binsize - x = np.arange(length) + 1 - 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) - plt.xlim(0.5, length + 0.5) - plt.title('Tauint') - plt.show() - - def plot_history(self): - N = self.N - x = np.arange(N) - tmp = [] - for i in range(self.replicas): - tmp.append(self.deltas[i] + self.r_values[i]) - y = np.concatenate(tmp, axis=0) # Think about including kwarg to look only at some replica - plt.errorbar(x, y, fmt='.', markersize=3) - plt.xlim(-0.5, N - 0.5) - plt.show() - - def dump(self, name, **kwargs): - """Dump the Jack to a pickle file 'name'. - - Keyword arguments: - path -- specifies a custom path for the file (default '.') - """ - if 'path' in kwargs: - file_name = kwargs.get('path') + '/' + name + '.p' - else: - file_name = name + '.p' - with open(file_name, 'wb') as fb: - pickle.dump(self, fb) - - -def generate_jack(obs, **kwargs): - full_data = [] - for r, name in enumerate(obs.names): - if r == 0: - full_data = obs.deltas[name] + obs.r_values[name] - else: - full_data = np.append(full_data, obs.deltas[name] + obs.r_values[name]) - - jacks = [] - if 'max_binsize' in kwargs: - max_b = kwargs.get('max_binsize') - if not isinstance(max_b, int): - raise TypeError('max_binsize has to be integer') - else: - max_b = 1 - - for b in range(max_b): - # binning if necessary - if b > 0: - n = full_data.size // (b + 1) - binned_data = np.zeros(n) - for i in range(n): - for j in range(b + 1): - binned_data[i] += full_data[i * (b + 1) + j] - binned_data[i] /= (b + 1) - else: - binned_data = full_data - n = binned_data.size - # generate jacks from data - mean = np.mean(binned_data) - tmp_jacks = np.zeros(n) - for i in range(n): - tmp_jacks[i] = (n * mean - binned_data[i]) / (n - 1) - jacks.append(tmp_jacks) - - # Value is not correctly reproduced here - return Jack(obs.value, jacks) - - -def derived_jack(func, data, **kwargs): - """Construct a derived Jack according to func(data, **kwargs). - - Parameters - ---------- - func -- arbitrary function of the form func(data, **kwargs). For the automatic differentiation to work, - all numpy functions have to have the autograd wrapper (use 'import autograd.numpy as np'). - data -- list of Jacks, e.g. [jack1, jack2, jack3]. - - Notes - ----- - For simple mathematical operations it can be practical to use anonymous functions. - For the ratio of two jacks one can e.g. use - - new_jack = derived_jack(lambda x : x[0] / x[1], [jack1, jack2]) - - """ - - # Check shapes of data - if not all(x.N == data[0].N for x in data): - raise Exception('Error: Shape of data does not fit') - - values = np.zeros(len(data)) - for j, item in enumerate(data): - values[j] = item.value - new_value = func(values, **kwargs) - - jacks = [] - for b in range(data[0].max_binsize): - tmp_jacks = np.zeros(data[0].N[b]) - for i in range(data[0].N[b]): - values = np.zeros(len(data)) - for j, item in enumerate(data): - values[j] = item.jacks[b][i] - tmp_jacks[i] = func(values, **kwargs) - jacks.append(tmp_jacks) - - return Jack(new_value, jacks)