From 0b158f87316f8b663fde879284cae01d4f8e092c Mon Sep 17 00:00:00 2001
From: Fabian Joswig <fabian.joswig@ed.ac.uk>
Date: Thu, 21 Oct 2021 11:53:50 +0100
Subject: [PATCH] fits fit_exp removed

---
 .github/workflows/flake8.yml |  2 +-
 CHANGELOG.md                 |  1 +
 pyerrors/fits.py             | 28 ----------------------------
 3 files changed, 2 insertions(+), 29 deletions(-)

diff --git a/.github/workflows/flake8.yml b/.github/workflows/flake8.yml
index 0766352d..e75a5ae1 100644
--- a/.github/workflows/flake8.yml
+++ b/.github/workflows/flake8.yml
@@ -1,4 +1,4 @@
-name: flake8 Lint
+name: flake8
 
 on:
   push:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 259f61ef..92681a4a 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -17,6 +17,7 @@ All notable changes to this project will be documented in this file.
 - The function `plot_corrs` was deprecated as all its functionality is now contained within `Corr.show`
 - The kwarg `bias_correction` in `derived_observable` was removed
 - Obs no longer have an attribute `e_Q`
+- Removed `fits.fit_exp`
 
 ## [1.1.0] - 2021-10-11
 ### Added
diff --git a/pyerrors/fits.py b/pyerrors/fits.py
index 1781df37..d57445bd 100644
--- a/pyerrors/fits.py
+++ b/pyerrors/fits.py
@@ -512,34 +512,6 @@ def fit_lin(x, y, **kwargs):
         raise Exception('Unsupported types for x')
 
 
-def fit_exp(data, **kwargs):
-    """Fit a single exponential to a discrete time series of Obs without errors.
-
-    Keyword arguments
-    -----------------
-    shift -- specifies the absolute timeslice value of the first entry of data (default 0.0)
-             only important if one is interested in the matrix element, for the mass this is irrelevant.
-    """
-    if 'shift' in kwargs:
-        shift = kwargs.get("shift")
-    else:
-        shift = 0
-    length = len(data)
-    xsum = 0
-    xsum2 = 0
-    ysum = 0
-    xysum = 0
-    for i in range(shift, length + shift):
-        xsum += i
-        xsum2 += i ** 2
-        tmp_log = np.log(np.abs(data[i - shift]))
-        ysum += tmp_log
-        xysum += i * tmp_log
-    res0 = -(length * xysum - xsum * ysum) / (length * xsum2 - xsum * xsum)  # mass
-    res1 = np.exp((xsum2 * ysum - xsum * xysum) / (length * xsum2 - xsum * xsum))  # matrix element
-    return [res0, res1]
-
-
 def qqplot(x, o_y, func, p):
     """ Generates a quantile-quantile plot of the fit result which can be used to
         check if the residuals of the fit are gaussian distributed.