jackknifing removed

pyerrors/jackknifing.py

@@ -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)